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APPENDIX A
Two-Page Reports from FY19-20
University olldaho ^#lnsz'l,College of Art and Architecture
Using IR Cameras in Building Controls - Phase II
Project Duration: 12 months Prcject Cost: Total Funding $52,500
OBJEGTIVE
In 2OL9, the Integrated Design tab (IDL)
demonstrated the technical feasibility of usinga low-cost infrared (IR) camera to provide
better temperature management of rooms.
The objective of the second phase is to bringthe concept of IR thermostats closer to
commerciallzation. The research team is
working to miniaturize, automate, and add
glare detection and occupant counts to the IR
thermostat. The research includes setting up a
test chamber to compare the new product to a
traditional thermostat, while developing a
business case.
BUSINESS VALUE
The IR thermostat functions as an all-in-onesensor that is capable of measuring
temperatures and counting occupants. This
device can replace individual sensors, thus
limiting wiring costs and installation times. The
team is also working to add glare detection
algorithms so that this device can dim lights
and adjust motorized blinds. The lab aims to
commercialize the concept of combining three
sensors into one device to provide improved
comfoft and energy savings.
INDUSTRY NEED
Most buildings manage their heating or cooling
based solely on zone air temperature, when in
reality human comfort is dominated by
surrounding surface temperatures. A controlsystem that incorporates the surface
temperatures of a zone would allow for a wider
range of supply air temperatures to better
meet the comfoft needs of occupants. The
wider range of air temperatures has the
potential to reduce energy consumption and
can help to capitalize on operational features
such as natural ventilation, night-flush and
optimized set-points. Current occupancy
signals rely on COz sensors, which are
expensive and require frequent recalibration.
The IR thermostat will be able to count the
occupants in a room and provide accurate
levels of ventilation without the need for COz
sensors.
BAGKGROUND
The IDL measured the thermal comfort
parameters of several offices within Avista's
climate zones. The findings indicated that
current thermostat controls fail to provide
adequate comfort in offices and waste cooling
energy while doing so. Last year, the IDL
explored the possibility of using a thermal
camera to incorporate surface temperatures
into thermostat controls. This process was
carried out with a large camera mounted on a
tripod and manual calculations. The research
showed that using the IR camera to estimate
comfort and detect occupancy is feasible.
SGOPE
The primary focus of this phase of research isto automate and commercialize the IR
thermostat concept. To advance this concept,
the IDL will utllize a thimble-sized IR camera
on an automatic pan/tilt platform and some
micro processing boards. The research team
will set up communication between the IR
thermostat and a piece of HVAC equipment to
serve as a test-bed for analysis. This will close
the communication loop between the camera
and a heating or cooling device to condition a
space and provide a direct comparison to the
functioning of a traditional thermostat. The
team will convert the manual inputs of surface
temperatures and conditions into the standard
comfoft formula to deliver better heating and
cooling signals through standard Building
Automation Control network (BACnet)
protocols. IDL has used open-source machine
learning codes to identify occupant outlines
from pixelated IR images. The team will modify
The information contained in this document is proprietary and confidential.
and enhance this approach to use the camera
to count the number of occupants in a space
and to detect direct sunlight (glare) coming
through any windows to adjust blinds and
optimize cooling controls. The research team is
working with the University of Idaho’s College
of Business and Office of Technology Transfer
on commercialization options.
Task 1: Project Planning and Reporting
Task 2: Set up HVAC Equipment
Task 3: Automate IR Signal Processing
Task 4: Develop Glare Detection
Algorithm
Task 5: Run Comparison with Traditional
Thermostat
Task 6: Document Results
DELIVERABLES
Project team has set up a piece of HVAC
equipment with a call and response to
the IR thermostat that can either heat
or cool the experimental chamber.
Project team has installed the device on
a pan/tilt mechanism and has
scheduled the photography and signal
processing for autonomous
deployment.
Project team has translated glare
readings to BACnet signal objects for
blind controls.
Project team has compared
performance of using an IR thermostat
versus a traditional thermostat to
manage the temperature of an
experimental chamber and can quantify
the difference in energy used.
Project team is able to articulate the
process, move towards
commercialization, and produce a final
report of findings for Avista. The team
has completed a current literature
review of the relevant technology in this
sector.
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Damon Woods
Organization University of Idaho Integrated Design Lab
Contact #[phone]
Email [e-mail]
RESEARCH ASSISTANTS
Name Jubin Mathai
Organization University of Idaho Integrated Design Lab
Email [e-mail]
SCHEDULE
TASK
TIME
ALLOCATED
(Months)
START
DATE
FINISH
DATE
Project planning 12 09/01/19 08/30/20
Set up HVAC equipment 2 09/01/18 01/01/20
Automate IR signal
processing 4 01/01/20 05/01/20
Develop glare detection algorithm 6 10/01/19 04/01/20
Run comparison with
traditional thermostat 2 05/01/20 07/01/20
Document results 2 07/01/20 08/30/20
Gamification of Energy Use Feedback
Project Duration: 12 months Project Cost: Total Funding $108,736
OBJECTIVE
In human systems, feedback is essential to
understanding the relationship between effort,
error, and optimal (or at least successful)
performance. If human users can be made
explicitly aware of the essential elements of
their performance, they can modify that
performance. The intent in this project is to use
the gaming metaphor to make attention to
feedback appealing and frequent. The most
effective feedback systems then offer
behavioral options, i.e., actions a user can take
to change his or her future outcomes. A slate
of options that affect usage, but also bring
users to educational modules and helpful
products will be recommended.
Gamification can stimulate greater
attentiveness to energy use; attention can lead
to reductions in usage. A gamification system
will give the utility the ability to incentivize
conservation, and can also reward citizen
continuing education about energy systems
and products.
To meet increasing demands and increasing
costs, the industry would benefit from
customers’ conservation behaviors. We believe
that the utility would benefit from better
monitoring of their energy usage by
customers. As meter systems become
“smarter”, information available to customers
can be more granular, more up-to-date, and
thus potentially actionable. Presumably, those
customer actions will lead to optimization of
their usage. Optimization can be in the service
of their own savings but could also serve their
other pro-social interests (donations of savings
to worthy causes, environmental protection,
etc.).
BACKGROUND
SCOPE
Task 1: --- A general review of incentive
systems and gamification. We will review past
and current attempts to use feedback-based
systems by utilities. These attempts have
been well-crafted but could not be sustained.
We want to learn from those attempts without
repeating the mistakes.
Task 2: -- We will review the literature on
successful incentive strategies. Then, survey
customers to create a user profile system that
will be the basis of customer gamification
choices.
The information contained in this document is proprietary and confidential.
Task 3: -- We will identify the utility data
collection capabilities that would allow
feedback to customers. E.g., what information
can be made available (taking into account
security concerns)? Then, we will provide
evidence of concept, as follows.
Task 4: We will user-test the system and
make appropriate modifications. Small
samples of potential users will be identified at
appropriate venues.
DELIVERABLES
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Richard Reardon
Organization University of Idaho, Dept. Psychology/Comm
Contact #208-292-2523
Email [e-mail]
Name Julie Beeston
Organization University of Idaho, Dept. of Computer Science
Contact #208-292-2671
Email [e-mail]
RESEARCH ASSISTANTS
Name Kellen Probert
Organization University of Idaho, Psyc Human Factors
Email [e-mail]
Name Jode Keehr
Organization University of Idaho, Psyc Human Factors
Email [e-mail]
Name David Beeston
Organization Private Utility Consultant
Email [e-mail]
Name Undergraduate (to be named)
Organization University of Idaho, Dept. of Computer Science
Email tba
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
1. Research on incentive
systems and gamification; report
prepared
11 months (the bulk gathered by
the 5th month)
9/19 7/20
2. Survey developed,
deployed; data analyzed
4 months to develop/deploy;
2 months of analysis
9/19 3/20
3. Mockups/prototypes
developed and tested 11 months 9/19 7/20
4. User testing 4 months 4/20 8/20

Designing and Evaluating an Energy Trading System for
Prosumers – Phase II
Project Duration: 12 months Project Cost: Total funding $96,164
OBJECTIVE
The objective of this project is to analyze and
develop a prototype system that would enable
Avista prosumers and consumers to trade
power on-demand with utility oversight, or
with the utility while also ensuring the utility’s
network resilient operation. In phase I of this
project, we completed the analysis and
implementation of the first prototype of this
application. In Phase II, we propose to design,
implement, and integrate a transaction
prioritization and pricing algorithm into this
prototype. Such algorithm would have the goal
of maximizing the economic benefit, welfare,
and availability of the electric power grid in the
presence of prosumers, while adequately
incentivizing prosumer-offered services where
and when needed. Such system will enable
Avista to create a new market for prosumer-
consumer power trading and to plan, manage,
and control such market and the flow of power
through its network.
Avista will benefit from such prototype in the
following ways:
Provide a platform for testing new
technologies and algorithms that would
enable larger-scale implementations of
such customer-driven trading markets.
Enable the collection of data on sharing
behaviors and trading patterns.
Enable data analytics that may lead to
increased efficiency in the distribution
system.
Enable the implementation of real-time
profitable and constraint-based electric
power pricing strategies.
The adoption of consumer-level electric energy
production and storage and of smart consumer
appliances is accelerating. Examples of
technologies leading this rapid adoption are
photovoltaics, small wind, electric vehicles,
and smart water heaters and freezers, among
other technologies. Within such a new
prosumer-enabled smart grid, enabling the
managed, but timely and selective trading and
transmission of power between prosumers
(producer-consumer) and with the utility may
enable a more efficient use of the power grid
as well as provide utility savings on generation
and transmission. For example, a temporary
increase in demand from a smart-grid-enabled
customer could be served from a grid-near
prosumer with stored capacity or solar
generation.
BACKGROUND
SCOPE
Task 1: Meet with Avista® to gather
system requirements.
Task 2: Review of the literature on
prioritization and pricing strategies at the
distribution level.
Research has been conducted in reviewing
past and current literature on LMP and
distribution Locational Marginal Price (DLMP).
Past project and transactive markets have also
been studied on how to incorporate a
prosumer-based market into the power system
for transaction support.
Studies have been reviewed for customer costs
on the power system. Base prices and cost of
energy production have been evaluated for a
per customer basis on the power system.
The model developed within Phase I of the
project has been successfully enhanced from
the IEEE 13 bus system to the IEEE 34 Bus
system (OpenDSS). This IEEE 34 Bus system
has been modified to incorporate customers
with renewable resources.
The prioritization algorithm will be developed
in a modern programming language such as
MATLAB or Python. Implementation is
progressing on schedule. Options to transfer
the algorithm to Python are being evaluated.
Transaction control from the database will
expand on the system implemented in the
Phase I portion of the project. This integration
is being implemented currently and requires
full system development from the database.
Different scenarios will be designed to study
the impact of different prices and operating
points of the model power system. Task 7 will
begin once Task 5 has completed.
The team has the individual parts (SQL query,
OpenDSS, and Python) in the testing phases.
Once all the components are autonomously
interacting with each other, tests will be
developed to observe the systems
performance.
Task 9: Produce user and administrator
usage guides
DELIVERABLES
Written final report.
Interim reports and online conferences
with Avista, including one mid-term
report.
Proof-of-concept software and user and
administrative documentation.
Proof-of-concept evaluation within a
small-scale simulated distribution
system.
Outlines of proposals for follow-on
funding to further develop and refine
the prototype.
PROJECT TEAM
PRINCIPAL INVESTIGATOR
Name Dr. Yacine Chakhchoukh
Contact #(208)-885-1550
Email [e-mail]
CO-PRINCIPAL INVESTIGATOR
Name Dr. Daniel Conte De Leon
Contact #(208)-885-6520
Email [e-mail]
Name Dr. Herbert L. Hess
Contact #(208)-885-4341
Email [e-mail]
Name Dr. Brian Johnson
Contact #(208)-885-6902
Email [e-mail]
Name Dr. Hangtian Lei
Contact #(208)-885-0952
Email [e-mail]
SCHEDULE
TASK
TIME
ALLOCATED START
DATE
FINISH
DATE
Task 1 4.5 months 08/18/19 01/01/20Task 2 5.5 months 08/18/19 01/31/20
Task 3 5.5 months 08/18/19 01/31/20Task 4 5.5 months 08/18/19 01/31/20Task 5 4 months 12/01/19 03/01/20
Task 6 3 months 03/01/20 06/01/20Task 7 5 months 03/01/20 08/01/20
Task 8 3 months 06/01/20 08/31/20
Task 9 1 month 08/01/20 08/31/20
APPENDIX B
Request for Proposal
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
Request for Proposal (RFP)
Contract No. R-40239
for
Avista Energy Research (AER) Initiative
INSTRUCTIONS AND REQUIREMENTS
Proposals are due by 4:00 p.m. Pacific Prevailing Time (PPT), April 21, 2019 (the “Due Date”)
Avista Corporation is an energy company involved in the production, transmission and distribution of
energy as well as other energy-related businesses. Avista Utilities is the operating division that provides
electric service to approximately 374,000 customers and natural gas to approximately 336,000
customers. Avista’s service territory covers 30,000 square miles in eastern Washington, northern Idaho
and parts of southern and eastern Oregon, with a population of 1.5 million. Avista’s stock is traded
under the ticker symbol “AVA”. For more information about Avista, visit www.avistautilities.com.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 2 of 9
Avista Corporation (“Avista”)
This Request for Proposal (“RFP”) may contain information that is marked as confidential and proprietary to
Avista (“Confidential Information” or “Information”). Under no circumstances may the potential Bidder
receiving this RFP use the Confidential Information for any purpose other than to evaluate the requirements of
this RFP and prepare a responsive proposal (“Proposal”). Further, Bidder must limit distribution of the
Information to only those people involved in preparing Bidder’s Proposal.
If Bidder determines that they do not wish to submit a Proposal, Bidder must provide a letter to Avista
certifying that they have destroyed the Confidential Information, or return such Information to Avista and
certify in writing that they have not retained any copies or made any unauthorized use or disclosure of such
information.
If Bidder submits a Proposal, a copy of the RFP documents may be retained until Bidder has received notice
of Avista’s decision regarding this RFP. If Bidder has not been selected by Avista, Bidder must either return
the Information or destroy such Information and provide a letter to Avista certifying such destruction.
Avista and Bidder will employ the same degree of care with each other’s Confidential Information as they use
to protect their own Information and inform their employees of such confidentiality obligations.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 3 of 9
Instructions and Requirements
1.0 PURPOSE
In response to the Idaho Public Utilities Commission Order No. 32918, Avista Corporation will fund up to
$300,000 per year of applied research that will further promote broad conservation goals of energy efficiency
and curtailment. Specifically, Avista is seeking a qualified four year institution in the state of Idaho to
provide such applied research (the “Services”). In light of the rapidly changing utility landscape, Avista
would be interested in funding research projects which are forward thinking and would assist the utility in the
development of product and services which provide an energy efficient commodity to its customers. The
applied research and development projects can be one or multiple years and can be used to support university
research programs, facility and studies.
The following institutions are eligible to submit Avista Energy Research (AER) initiative proposals.
1. University of Idaho 2. Boise State University 3. Idaho State University
Persons or institutions submitting a Proposal will be referred to as “Bidder” in this RFP; after execution of a
contract, the Bidder to whom a contract is awarded, if any, will be the name of the university (“Institution”).
2.0 STATEMENT OF WORK
The attached Statement of Work (“SOW”) specifies the activities, deliverables and/or services sought by
Avista. This SOW will be the primary basis for the final SOW to be included under a formal contract, if a
contract is awarded.
3.0 RFP DOCUMENTS
Attached are the following RFP Documents:
Appendix A – Proposal Cover Sheet
Appendix B – Sponsored Research and Development Project Agreement
4.0 CONTACTS / SUBMITTALS / SCHEDULE
4.1 All communications with Avista, including questions (see Section 5.1), regarding this RFP must be
directed to Avista’s Sole Point of Contact (“SPC”):
Russ Feist
Avista Corporation
1411 East Mission Avenue
PO Box 3727, MSC-33
Spokane, WA 99220-3727
Telephone: [phone]
Fax: [phone]
E-Mail: [e-mail]
4.2 Proposals must be received no later than 4:00 PM Pacific Prevailing Time (“PPT”), on April 22,
2019 (“Due Date”). Bidders should submit an electronic copy of their Proposal to
[e-mail]. In addition to an electronic copy, Bidders may also fax their Proposal to 509-
495-8033, or submit a hard copy to the following address:
Avista Corporation
Attn: Greg Yedinak Supply Chain Management (MSC 33)
1411 E. Mission Ave
PO Box 3727
Spokane, WA 99220-3727
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 4 of 9
No verbal or telephone Proposals will be considered and Proposals received after the Due Date may
not be evaluated.
4.3 RFP Proposed Project Schedule
March 18, 2019 Avista issues RFP
April 9, 2019 Bidder’s Questions/Requests for Clarification Due
April 15, 2019 Avista’s Responses to Clarifications Due Date
April 22, 2019 Proposals Due
May 10, 2019 Successful Bidder selection and announcement
June 28, 2019 Contract and Statement of Work Executed
5.0 RFP PROCESS
5.1 Pre-proposal Questions Relating to this RFP
Questions about the RFP documents (including without limitation, specifications, contract terms or
the RFP process) must be submitted to the SPC (see Section 4.1), in writing (e-mailed, faxed, or
addressed in accordance with Section 4.2, by the Due Date. Notification of any substantive
clarifications provided in response to questions will be provided via email to all Bidders.
5.2 Requests for Exceptions
Bidder must comply with all of the requirements set forth in the documents provided by Avista as
part of this RFP (including all submittals, contract documents, exhibits or attachments). Any
exceptions to these requirements must be: (i) stated separately, (ii) clearly identify the exceptions
(including the document name and section), and (iii) include any proposed alternate language, etc.
Failure by Bidder to provide any exceptions in its Proposal will constitute full acceptance of all
documents provided by Avista as part of this RFP. While Avista will not consider alternate language,
etc. that materially conflicts with the intent of this RFP, Avista may consider and negotiate the
inclusion of terms that would be supplemental to the specific document if such terms reasonably
relate to the scope of this RFP.
5.3 Modification and/or Withdrawal of Proposal
5.3.1 By Bidder: Bidder may withdraw its Proposal at any time. Bidder may modify a submitted
Proposal by written request provided that such request is received by Avista prior to the Due
Date. Following withdrawal or modification of its Proposal, Bidder may submit a new
Proposal provided that such new Proposal is received by Avista prior to the Due Date and
includes a statement that Bidder’s new Proposal amends and supersedes the prior Proposal.
5.3.2 By Avista: Avista may modify any of the RFP documents at any time prior to the Due Date.
Such modifications will be issued simultaneously to all participating Bidders.
5.4 Proposal Processing
5.4.1 Confidentiality: It is Avista’s policy to maintain the confidentiality of all Proposals
received in response to an RFP and the basis for the selection of a Bidder to negotiate a
definitive agreement.
5.4.2 Basis of Any Award: This RFP is not an offer to enter into an agreement with any party.
The contract, if awarded, will be awarded on the basis of Proposals received after
consideration of Bidder’s ability to provide the services/work, quality of personnel, extent
and quality of relevant experience, price and/or any other factors deemed pertinent by
Avista, including Bidder’s ability to meet any schedules specified in the Statement of Work.
5.4.3 Pre-award Expenses: All expenses incurred by Bidder to prepare its Proposal and
participate in any required pre-bid and/or pre-award meetings, visits and/or interviews will
be Bidder’s responsibility.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 5 of 9
5.4.4 Proposal Acceptance Term: Bidder acknowledges that its Proposal will remain valid for
a period of 60 days following the Due Date unless otherwise extended by Avista.
5.5 Contract Execution
The successful Bidder shall enter into a contract that is substantially the same as the Sponsored
Research and Development Project Agreement governing the performance of the Services/Work
applicable under this RFP included as Appendix B. However, those Universities that have prior
written agreements with Avista, may mutually agree to utilize those agreements with an extension
and some modifications to the documents.
6.0 PROPOSAL REQUIREMENTS AND SUBMITTALS
Bidder’s Proposal must conform to the following outline and address all of the specified content to facilitate
Avista’s evaluation of Bidder’s qualifications; approach to performing the requested Services/Work; and other
requirements in the SOW. Proposals will be evaluated on overall quality of content and responsiveness to the
purpose and specifications of this RFP, including the information set forth in Section 6.5 below.
6.1 Proposal Process
Each eligible institution will be limited to TEN specific proposal submittals. One representative of the
eligible institutions will be responsible for submitting all of the proposals.
The proposal must not exceed 6 pages not including the appendices. The proposal shall be in 11 point
font, 1.5 spaced and one inch margins. The original and one electronic copy of the proposal (PDF –
Form) must be provided to Avista’s point of contact listed herein.
6.2 Proposal Submittals The following items are required with Bidder’s Proposal. Each proposal
shall contain the following project elements.
1. Name of Idaho public institution;
2. Name of principal investigator directing the project;
3. Project objective and total amount requested (A general narrative summarizing the approach
to be utilized to provide the required services);
4. Resource commitments, (number of individuals and possible hours for services);
5. Specific project plan (An outline of work procedures, technical comments, clarifications
and any additional information deemed necessary to perform the services);
6. Potential market path;
7. Criteria for measuring success;
8. Budget Price Sheet / Rate Schedule;
9. Proposal Exceptions to this RFP (if any);
10. Appendix A – Proposal Cover Sheet (last 2 pages of this document)
11. Appendix C: Facilities and Equipment
12. Appendix D: Biographical Sketches and Experience of the principle investigators and / or
primary research personnel for each project (if different individuals for each project
submitted)
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 6 of 9
6.3 Proposal Cover Sheet
Bidder must fill out, sign and date the attached Proposal Cover Sheet. The signatory must be a
person authorized to legally bind Bidder’s company to a contractual relationship (e.g. an officer of
the company).
6.4 Institution Information
Institution Qualifications
Bidder shall provide information on projects of similar size and scope that Bidder has
undertaken and completed within the last five years. Please include a list of references on
Appendix A that could be contacted to discuss Bidders involvement in these projects.
Institution Resources
Identify any unique or special equipment, intellect, hardware, and software or personnel
resources relevant to the proposed Services that Bidder’s firm possesses(list in Appendix D).
Project Personnel Qualifications
Provide a proposed organization chart or staffing list for a project of this size and scope and
identify the personnel who will fill these positions. If applicable, identify project managers who
will be overseeing the Services and submit their resume identifying their work history, (please
see Section 6.2, question #4).
Approach to Subcontracting
If Bidder’s approach to performing the Services will require the use of subcontractors, include
for each subcontractor: (a) a description of their areas of responsibility, (b) identification of the
assigned subcontractor personnel, (c) resumes of key subcontractor personnel, (d) a summary
of the experience and qualifications of the proposed subcontracting firms in work similar to
that proposed, and (e) a list of references for such work.
6.5 Evaluation Criteria
Avista will evaluate each proposal based upon the following criteria:
6.5.1 Project Requirements
Strength of Proposal
Responsiveness to the RFP
Creativity in Leveraging Resources
Samples of Work Products
Overall Proposal (Complete, Clear, Professional)
6.5.2 Strength & Cohesiveness of the Project Team
Overall ability to manage the project
Technical ability to execute the Services
Research/analysis ability
Project milestones with clear stage and gates (annually)
Overall team cohesiveness
6.5.3 Qualifications and Experience
Experience working with electric utilities
Project management and multi-disciplined approaches
Experience working with organizations in a team atmosphere
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 7 of 9
7.0 RESERVATION OF AVISTA RIGHTS:
Avista may, in its sole discretion, exercise one or more of the following rights and options with respect
to this RFP:
Modify, extend, or cancel this RFP at any time to obtain additional proposals or for any other reason
Avista determines to be in its best interest;
Issue a new RFP with terms and conditions that are the same, similar or substantially different as
those set forth in this or a previous RFP in order to obtain additional proposals or for any other reason
Avista determines to be in its best interest;
Waive any defect or deficiency in any proposal, if in Avista’s sole judgment, the defect or deficiency
is not material in response to this RFP;
Evaluate and reject proposals at any time, for any reason including without limitation, whether or not
Bidder’s proposal contains Requested Exceptions to Contract Terms;
Negotiate with one or more Bidders regarding price, or any other term of Bidders’ proposals, and
such other contractual terms as Avista may require, at any time prior to execution of a final contract,
whether or not a notice of intent to contract has been issued to any Bidder and without reissuing this
RFP;
Discontinue negotiations with any Bidder at any time prior to execution of a final contract, whether
or not a notice of intent to contract has been issued to Bidder, and to enter into negotiations with any
other Bidder, if Avista, in its sole discretion, determines it is in Avista’s best interest to do so;
Rescind, at any time prior to the execution of a final contract, any notice of intent to contract issued
to Bidder.
[END OF REQUEST FOR PROPOSAL INSTRUCTIONS AND REQUIREMENTS]
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 8 of 9
APPENDIX A - Proposal Cover Sheet
Organization Name:
Organization Form:
(sole proprietorship, partnership, Limited Liability Company, Corporation, etc.)
Primary Contact Person: ____________________________ Title: __________________________________
Address:
City, State, Zip:
Telephone: Fax: Federal Tax ID#
E-mail Address:
Name and title of the person(s) authorized to represent Bidder in any negotiations and sign any contract that may
result (“Authorized Representative”):
Name: Title:
If classified as a contractor, provide contractor registration/license number applicable to the state in which
Services are to be performed. ____________________________________
(please verify numbers) that Avista may contact to
verify the quality of Bidder’s previous work in the proposed area of Work.
REFERENCE No. 1:
Organization Name:
Contact Person:
Project Title:
Telephone:
Fax:
Email:
REFERENCE No. 2:
Organization Name:
Contact Person:
Project Title:
Telephone:
Fax:
Email:
REFERENCE No. 3:
Organization Name: Telephone:
Fax:
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-40239 Page 9 of 9
Contact Person:
Project Title:
Email:
By signing this page and submitting a Proposal, the Authorized Representative certifies that the following
statements are true:
1. They are authorized to bind Bidder’s organization.
2. No attempt has been made or will be made by Bidder to induce any other person or organization to submit
or not submit a Proposal.
3. Bidder does not discriminate in its employment practices with regard to race, creed, age, religious
affiliation, sex, disability, sexual orientation or national origin.
4. Bidder has not discriminated and will not discriminate against any minority, women or emerging small
business enterprise in obtaining any subcontracts, if required.
5. Bidder will enter into a contract with Avista and understands that the final Agreement and General
Conditions applicable to the Scope of Work under this RFP will be sent for signature under separate cover.
6. The statements contained in this Proposal are true and complete to the best of the Authorized
Representative’s knowledge.
7. If awarded a contract under this RFP, Bidder:
(i) Accepts the obligation to comply with all applicable state and federal requirements, policies,
standards and regulations including appropriate invoicing of state and local sales/use taxes (if any) as
separate line items;
(ii) Acknowledges its responsibility for transmittal of such sales tax payments to the taxing authority;
(iii) Agrees to provide at least the minimum liability insurance coverage specified in Avista’s attached
sample Agreement, if awarded a contract under this RFP.
8. If there are any exceptions to Avista’s RFP requirements or the conditions set forth in any of the RFP
documents, such exceptions have been described in detail in Bidder’s Proposal.
9. Bidder has read the “Confidentiality Notice” set forth on the second page of these “INSTRUCTIONS
AND REQUIREMENTS” and agrees to be bound by the terms of same.
Signature: Date:
*** THIS PAGE MUST BE THE TOP PAGE OF BIDDER’S PROPOSAL ***
APPENDIX C
University of Idaho Agreements

APPENDIX A - Proposal Cover Sheet
Bidder Information
Organization Name: University of Idaho Organization Form: (sole proprietorship, partnership, Limited Liability Company, Corporation, etc.)
Primary Contact Person: ____________________________ Title: __________________________________ Address: City, State, Zip: Telephone: Fax: Federal Tax ID# E-mail Address: Name and title of the person(s) authorized to represent Bidder in any negotiations and sign any contract that may result (“Authorized Representative”):
Name: Title: If classified as a contractor, provide contractor registration/license number applicable to the state in which Services are to be performed. ____________________________________ Provide at least three references with telephone numbers (please verify numbers) that Avista may contact to verify the quality of Bidder’s previous work in the proposed area of Work. REFERENCE No. 1: Organization Name:
Contact Person:
Project Title:
Telephone:
Fax:
Email:
Organization Name: Contact Person: Project Title:
Telephone: Fax: Email:
Organization Name: Contact Person: Project Title:
Telephone: Fax: Email:

RFP No. R-41387 Page 2
By signing this page and submitting a Proposal, the Authorized Representative certifies that the following
statements are true:
1. They are authorized to bind Bidder’s organization.
2. No attempt has been made or will be made by Bidder to induce any other person or organization to submit
or not submit a Proposal.
3. Bidder does not discriminate in its employment practices with regard to race, creed, age, religious affiliation, sex, disability, sexual orientation or national origin.
4. Bidder has not discriminated and will not discriminate against any minority, women or emerging small business enterprise in obtaining any subcontracts, if required.
5. Bidder will enter into a contract with Avista and understands that the final Agreement and General Conditions applicable to the Scope of Work under this RFP will be sent for signature under separate cover.
6. The statements contained in this Proposal are true and complete to the best of the Authorized Representative’s knowledge.
7. If awarded a contract under this RFP, Bidder:
(i) Accepts the obligation to comply with all applicable state and federal requirements, policies, standards and regulations including appropriate invoicing of state and local sales/use taxes (if any) as separate line items;
(ii) Acknowledges its responsibility for transmittal of such sales tax payments to the taxing authority;
(iii) Agrees to provide at least the minimum liability insurance coverage specified in Avista’s attached sample Agreement, if awarded a contract under this RFP.
8. If there are any exceptions to Avista’s RFP requirements or the conditions set forth in any of the RFP
documents, such exceptions have been described in detail in Bidder’s Proposal.
9. Bidder has read the “Confidentiality Notice” set forth on the second page of these “INSTRUCTIONS AND REQUIREMENTS” and agrees to be bound by the terms of same.
Signature: Date:

RFP No. R-41387 Page 3

Name of Idaho public institution:
University of Idaho
Name of principal investigator directing the project;
PI: Damon Woods
Project Title – Objective – Amount Requested
Using IR Cameras in Building Controls – Phase II
Project objective and total amount requested
Amount Requested: $52,500
Objective The goal of using IR cameras in building controls is to bring the concept of infrared-enhanced
thermostats closer to commercialization. This project builds on two previously funded Avista grants
concerned with operative temperature control (controls that take into account the surrounding surface
temperatures in a room). The Integrated Design Lab is currently testing the feasibility of using a low-cost
infrared camera to estimate the mean radiant temperature in a room. At the end of phase I, the team will
process the data from the camera into a prediction of how comfortable the space is and then send that
information as a standard control signal. For phase II, the IDL team would like to perform product testing by
routing that control signal into a piece of HVAC hardware. This will enable the IR-camera thermostat to
actually manage a space’s conditions based on the surface temperatures.
In 2017, the Integrated Design Lab received an Avista AER grant to measure indoor temperature
conditions at offices throughout Idaho. The findings suggested that because current thermostats only control
for air temperature, many offices are uncomfortably cool and the HVAC system is inefficiently run. Energy
models were used to estimate potential savings of managing a building’s HVAC on a more holistic level by
including the surface temperatures in the controls. In 2018, the IDL received a second Avista AER grant to
pursue proof of concept for an enhanced thermostat that could incorporate information about the surface
temperatures into its controls. This project is underway and is showing promising results. Currently, the
camera is able to interpret the room as a grid of temperatures, and the team has finalized a set of equations
that can be used to pinpoint estimated thermal comfort values at any location in the room.
With further development, this IR-enhanced thermostat device could also be used to detect
occupancy and glare. This will reduce the number of sensors that are often required in new buildings.
Sensing glare and occupancy will be coordinated through further data processing of the images to detect
movement and pixels of high light values. The IDL team has experience in the fields of image processing,
building control commissioning, and measurement of operative temperatures – all based on open-source code
platforms. IDL has a test site and the tools required to carry out this experiment. It is the logical next step for
continuing the development of operative temperature based controls.

RFP No. R-41387 Page 4

Resource commitments, (number of individuals and possible hours for services):

Personnel Hours estimate Description
publication plan and market path approach
management, and daily execution of tasks

Specific project plan The first step of the new research will be to set up a piece of HVAC equipment in an experimental
chambers that can respond to the control signals from the IR camera thermostat. This will “close the loop”
between the controller and a piece of HVAC hardware. This HVAC hardware will start with a simple device
such as a heating element. Once the heating element responds consistently, the team will attempt to connect a
more sophisticated piece of equipment such as a packaged terminal air conditioner or heat pump.
Once the HVAC equipment is shown to respond to feedback from the IR thermostat, the team will
begin to vary the camera position. Occupancy signals will be incorporated into the HVAC controls and
response. This will lay the groundwork for the automation of the process. Automation will include mounting
the camera on a pan/tilt mechanism so that its position, photography, and processing can be scheduled to
operate autonomously.
While the HVAC response and automation features are being pursued, the team will simultaneously
be working to incorporate glare detection from the camera so that it might be used for motorized blind
controls. In the past, the research team at IDL has used High Dynamic Range (HDR) photography in a room
to estimate the visual comfort in the room. The estimation in the past was carried out by searching the image
pixels for variances in brightness by using a script in the open-source software R. For this project, the IDL
team will seek to leverage this past work in order to generate a glare index by using the IR camera. Once the
team is able to convert the image pixels into a glare index, we will seek to generate a BACnet control signal
that could be read by motorized blinds.
The final phase of research will include a set of experiments that provide a direct comparison
between this infrared control and a traditional thermostat to gauge its effectiveness. The team will set up a
number of scenarios of varying temperatures and occupancy to estimate the amount of energy that could be
saved. The team will present the results to Avista in August, pursue publication in an academic journal, and
reach out to potential investors to commercialize the technology.

RFP No. R-41387 Page 5
Task 1 Project planning and reporting – Conduct team meetings and ongoing project updates, reports and
deliverables as required by Avista staff, project management contractor and the PUC. Ongoing throughout
the project.
Task 2 Set up HVAC equipment – The team will link the IR thermostat to an HVAC terminal unit that is
able to respond to BACnet control signals to condition the experimental chambers; 4 months.
Task 3 Automate IR signal processing – The camera will be set up to scan the room and take pictures at
automatic intervals and process this into a control signal on a regular schedule; 4 months.
Task 4 Develop glare detection algorithm – In parallel with tasks 2 and 3, IDL will use the operational data
collection to correlate the IR camera files to readings of glare within the space; 6 months.
Task 5 Run comparison with traditional thermostat – The research team will compare the signal response
between the IR thermostat and a traditional thermostat to gauge energy savings; 2 months.
Task 6 Document results – The final task will be to prepare for commercialization with this product and
seek outside investment. The team will provide a final report and presentation to Avista as well as pursue
publication in an academic journal such as Energy and Buildings; 2 months.

Potential market path
Building operations could benefit greatly from MRT and occupancy sensing controllers. Controls
based on MRT would allow for wider air temperature setpoints in the summer, thus saving cooling energy
each year. Increasing the cooling setpoint by 5oF has been shown to reduce total HVAC energy by 27%
(Hoyt et al. 2014). A study by the Pacific Northwest National Lab estimated slightly more conservative
savings: a 2oF adjustment on both heating and cooling setpoints led to a fairly uniform HVAC energy savings
of 12-20% (Fernandez et al. 2012). Nationwide, such savings would be equivalent to 370-615 trillion Btu
saved annually (EIA 2012). For commercial office buildings in the Pacific Northwest, HVAC energy is
typically 35% of the annual load (EIA 2012), an MRT thermostat implementation could result in total annual
IR Thermostat Phase II Timeline
Se
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1. Project Planning/Reporting
2. Set up HVAC equipment ●
3. Automate IR signal processing ●
4. Develop glare detection algorithim ●
5. Run comparison with traditional thermostat ●
6. Document results ●
2019 | 2020

RFP No. R-41387 Page 6
energy savings of 4-7% of annual energy consumption per building (Hoyt et al. 2014). A recent baseline
study of buildings in the Pacific Northwest found average office building annual energy use to be 112
kBtu/ft2 with an average office building size of 20,000 ft2. (Baylon, et al., 2008). Given these assumptions,
integrating an MRT controller could save 25,000 – 45,000 kWh/yr for an average office building. This
represents only a general estimate, and firmer numbers will be established with the conclusion of the ongoing
phase of this research this August. In addition to energy savings, an affordable MRT sensor could
dramatically enhance occupant comfort, thus making it an attractive service to offer to clients.
This advance in controls coincides with Washington State’s latest energy code requiring occupancy
sensors in many new commercial buildings that now must use Dedicated Outdoor Air Systems (DOAS)
(Washington Administrative Code commercial buildings WAC 51-11C140360). Using IR cameras in lieu of
CO2 sensors could serve the same function of measuring occupancy with the added benefit of also measuring
the MRT of a space. The report will provide a step-by-step methodology including a demonstration of how
this sensor could be implemented at any site with code-compliant controls. As the new controller is
integrated into the building’s energy management system, either building managers or utility companies
could provide incentives for this type of controller. The market path may include the further development of
this hardware and open source software for various private companies to develop their own IR camera
controller.
Criteria for measuring success
• Project team has set up a piece of HVAC equipment with a call and response to the IR thermostat
that can either heat or cool the experimental chamber.
• Project team has installed the device on a pan/tilt mechanism and has scheduled the photography and
signal processing for autonomous deployment.
• Project team has translated glare readings to BACnet signal objects for blind controls.
• Project team has compared performance of using an IR thermostat versus a traditional thermostat to
manage the temperature of an experimental chamber and can quantify the difference in energy used.
• Project team is able to articulate the process, move towards commercialization, and produce a final
report of findings for Avista.

Budget Price Sheet / Rate Schedule
PI/Faculty Salaries (Baker and Woods) $ 10,475
PI/Faculty Benefits (Baker and Woods) 2,775
Graduate Student Salaries 12,800
Student Benefits 486

RFP No. R-41387 Page 7
Total $ 52,500

Budget Justification
***All Hourly Rates are averages over CY18 and CY19
Salaries -Support Requested from Ken Baker at 50 hours ($58.04/hr), Damon Woods at 215 hours
($35.50/hr), and one graduate student at 800 hours ($16.00/hr)
Fringe – Estimated based on the following rates: 26.5% Faculty, 3.8% Students
Tuition Remission - Estimated tuition costs for one graduate student for Fall of 2018 and Spring of 2019
$8,864
Equipment/Computers - Estimated for project period is $1,500 for BACnet controllers, HVAC terminal
equipment and miscellaneous connectors.
Travel- Estimated travel expenses for project period $1,000, to include: (1) trip to Spokane for for final
presentation to Avista for PI and graduate student.
F&A/Overhead - IDL is considered an on-campus unit of the University of Idaho with a federally negotiated
rate of 50.33%.
Proposal exceptions
Per section 5.2 of the RFP, the University has described exceptions to RFP requirements and conditions in
the letter dated 4/15/16 and included with Appendix A.
References
Arens E, Humphreys M, de Dear RJ, Zhang H. (2010). “Are ‘Class A’ temperature requirements realistic or
desirable?” Building and Environment 2010; 45 (1) :4–10.
ASHRAE. Standard 55-2013: “Thermal environmental conditions for human occupancy.” American Society
of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE).
Baylon, David., Robison, David., Kennedy, Mike. (2008). “Baseline Energy Use Index of the 2002-2004
Nonresidential Sector: Idaho, Montana, Oregon, and Washington.” Ecotope
EIA Commercial Building Energy Consumption Survey – E1. Major fuel consumption by end use, 2012
Fernandez, N. et al., (2012). “Energy Savings Modeling of Standard Commercial Building Re-tuning
Measures: Large Office Buildings” Pacific Northwest National Lab Report 21596.

RFP No. R-41387 Page 8
Haves, Philip., Xu, Peng,. (2007). “The Building Controls Virtual Test Bed – A Simulation Environment for
Developing and Testing Control Algorithms, Strategies, and Systems.” Proc: Building Simulation 2007.
Moe, K. (2010). “Thermally Active Surfaces in Architecture.” New York: Princeton Architectural Press.
Olesen, B.W., et. al. (2007). “Operative Temperature Control of Radiant Surface Heating and Cooling
Systems” Proceedings of Clima 2007 Wellbeing Indoors.
Hoyt, Tyler; Arens, Edward; & Zhang, Hui. (2014). “Extending air temperature setpoints: Simulated energy
savings and design considerations for new and retrofit buildings.” Building and Environment 2014; 9 (10).
Tom, Steve. "Managing energy and comfort." ASHRAE Journal 50.6 (2008): 18-27.
Washington Administrative Code commercial buildings WAC 51-11C140360.

i
Appendix C – Facilities and Equipment University of Idaho - Integrated Design Lab (UI-IDL) Facilities and Equipment
Overview The UI-IDL occupies about 4,000 SF in downtown Boise. It includes a large classroom, three conference rooms and office and laboratory space. The IDL maintains a library of over 900 devices for measurement
and data logging of energy efficiency and human comfort parameters. Computational platforms include windows, Linux and Unix machines, including high performance workstations and a modest computing cluster. The website (www.uidaho.edu/idl) serves as an outreach conduit to the public, hosting archived video lectures, research products, and an equipment listing. The UI-IDL maintains a wide range of software licenses and capabilities related to energy efficacy and human factors research, data analysis and processing, and visualization, design and graphics (see list below). IDL has experience in the fields of image processing, BACnet object creation, and measurement of operative temperatures – all based on open-source code platforms. IDL has a test site and the tools required to carry out this experiment.
Institutional Qualifications & Recent projects The UI-IDL team has completed over $6M in energy efficiency research and education since 2004 and has extensive experience managing projects of this scale and size. Selected projects of similar size and scope from previous five years. References and work samples available upon request.
University of Idaho – Integrated Design Lab Current Support: Title: Advanced Energy Efficiency Funding Source: Idaho Power Company Performance Period: 1/1/2019-12/31/2020 Total Amount: $286,000 Location of Project: Idaho Person-months per year committed (Calendar, Academic, Summer): 15.5 (CY) Title: Managing for Efficiency Funding Source: Avista Corporation Performance Period: Total Amount: $24,000
Location of Project: Idaho Person-months per year committed (Calendar, Academic, Summer): 0.4 (CY) Title: Advancing Energy Efficiency Funding Source: Northwest Energy Efficiency Alliance Performance Period: 1/1/2018-12/31/2019
Total Amount: $109,000 Location of Project: Idaho Person-months per year committed (Calendar, Academic, Summer): 4 (CY) UI-IDL Software Capabilities
Simulation Programs:

ii
• OpenStudio
• EnergyPlus • Radiance • Daysim
• Autodesk Ecotect • Therm • WUFI
• AGI32 • COMFEN • eQUEST • Autodesk Project Vasari • Simergy 3d modeling programs • Autodesk Revit • Autodesk Autocad • Trimble Sketchup • Rhinocerous
Climate Analysis Programs • IDL Climate Tools Spreadsheets (http://idlboise.com/design-tool/ui-idl-climate-design-resources-1st-2nd-
generation-toolsets) • Climate Consultant
Energy Data Analysis
• Energy Star Portfolio Manager
• E-Cam
• Universal Translator
• E-Tracker
• Energy Explorer Other Related
• R statistical studio
• MathWorks MATLAB

iii
Appendix D – Biographical Sketches L. Damon Woods, P.E. Title: Ph.D. Candidate
Organization: University of Idaho, Integrated Design Lab - Boise Phone: [phone] Email: [e-mail]
EDUCATION University of Idaho, Ph.D. (Mechanical Engineering), [Exp. 2018] Boise State University, Masters of Science M.S.M.E. (Mechanical Engineering), 2013 Montana State University- Bozeman, B.S.M.E (Mechanical Engineering), 2010 PROFESSIONAL EXPERIENCE 2013-pres. Research Support, (University of Idaho, Integrated Design Lab – Boise) Conducted academic research related to the built environment. 2014-2014 Adjunct Instructor, (Boise State University – Boise) Taught applied thermodynamics to mechanical engineering seniors. 2012-2013 Research and Development Trainee, (ALSTOM Power – Baden, Switzerland)
Modeled power production of gas turbines and analyzed performance metrics. 2010-2012 Graduate Research Assistant, (Boise State University, M.E. Department – Boise) Developed model to predict power production for new vertical axis wind turbine.
2011-2012 Math Instructor, (The Ambrose School – Meridian, ID) Taught Algebra II/Trigonometry to high school students.
REFEREED JOURNAL ARTICLES: A. Nezamdoost, E. Cooper and D. Woods, "Using a passive design toolset to evaluate low-cost cooling strategies for an industrial facility in a hot and dry climate Article reference," Energy and Buildings, Vol. 159, pp. 319-331, Jan 2018. https://doi.org/10.1016/j.enbuild.2017.11.011 MASTERS THESIS: D. Woods, “Simulation of Vawt and Hydrokinetic Turbines with Variable Pitch Foils” Thesis (M.S.) Boise State University, 2013. PEER-REVIEWED PUBLISHED PROCEEDINGS: D. Woods, T. Noble, B. Acker, R. Budwig and K. V. D. Wymelenberg, "Optimizing Economizer Operation by Virtual Commissioning through Remote Co-Simulation," in International Building Simulation Conference, San Francisco, CA, 2017. https://doi.org/10.26868/25222708.2017.514.
D. Woods, A. Mahic, K. VanDenWymelenberg, J. Jennings and J. Cole, "Simulation on Demand for Deep Energy Retrofits," in ACEEE Summer Study, Asilomar, CA, 2016. https://aceee.org/files/proceedings/2016/data/papers/3_376.pdf
D. Woods, J. Gardner, and K. Myers “Simulation of Vertical Axis Wind Turbines with Variable Pitch Foils”, ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). 6 B., 2013.
doi:10.1115/IMECE2013-65694 OTHER PRESENTATIONS:
• Conferences: o American Wind Energy Association Annual Conference Poster Presentation, Anaheim, CA:
Simulation of Vertical Axis Wind Turbines using Simulink 05/13/2013 o Energy Policy Institute Research Conference, San Francisco CA: Using the Time Delay of Radiant Systems to the Grid’s Advantage 09/05/2014
o Idaho Energy and Green Buildings Conference, Boise, ID:
 Model-Based Building Commissioning 11/1/2016

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 Cold Feet: Managing Controls and Condensation when Simulating Radiant Slab
Cooling. 11/1/2016
 Understanding Building Controls. 10/4/2017
• Trainings:
o Energy modeling workshop on using EnergyPlus - Sponsored by the Northwest Energy Efficiency Alliance
 Eastern Idaho Region: Delivered at Idaho National Lab Center for Advanced Energy Studies on 11/18/2016
 Northern Idaho Region: Delivered at the University of Idaho on 12/01/2016
PROFESSIONAL ORGANIZATIONS: o Professional Engineer: License # 17372 – State of Idaho
o American Society of Heating Refrigeration, and Air conditioning Engineers (ASHRAE)
 Member – Idaho Chapter.

Designing and Evaluating an Energy Trading System for Prosumers - Phase II
University of Idaho
Principal Investigator (PI) and Project Director: Dr. Yacine Chakhchoukh
Co-PIs.: Drs. Daniel Conte de Leon, Herbert L. Hess, Brian Johnson, Lei Hangtian.
Project Manager: Ms. Arvilla Daffin.
Total Amount Requested: $ 96,164
1. Project Objective
We propose to design, develop, and test a software application that would enable prosumers and
consumers to trade power on-demand or semi-automatically between themselves, with utility oversight, or
with the utility. Such system would enable prosumer-consumer power trading with some of the
characteristics of sharing economies and markets. The goal is that such system would lead, in the long run,
to semi-automated trading of power similarly to current financial high-speed trading systems between
smart-grid-enabled consumers, prosumers, and the utility.
In phase I of this project, we completed the analysis and implementation of the first prototype of this
application. In phase II, we propose to design, implement, and integrate into this prototype, a transaction
prioritization and pricing algorithm. Such algorithm would have the goal of maximizing the economic
benefit, welfare, and availability of the electric power grid in the presence of prosumers while adequately
incentivizing prosumer-offered services where and when needed. Such system will enable Avista to create
a new market for prosumer-consumer power trading and to plan, manage, and control such market and the
flow of power through its network.
Avista will benefit from such system in the following ways: (1) Provide a platform for testing new
technologies, applications, and algorithms that would enable larger scale implementations of such
customer-driven trading markets, (2) Enable the collection of data on consumer behaviors and trading
patterns, (3) Enable data analytics that may lead to increased efficiency and resiliency in the distribution
system, (4) Enable implementation of real-time profitable and constraint-based electric power pricing
strategies.
2. Resource Commitment
Resource commitments for this project include the following:
• PI and co-PIs research and student mentoring time as part of their normal academic duties.
• One graduate student in Electrical Engineering as a funded Research Assistant (in budget request).
• One graduate student in Computer Science as a funded Research Assistant (in budget request).
• Rental for a year of necessary computing equipment time and software licenses (in budget request).
• Use of University of Idaho space and facilities and academic and network resources (as F&A).
3. Student Involvement
This project proposes to involve students in every aspect of the research: project definition, modeling of
the system and its components, designing, developing, and testing the prioritization and pricing algorithm
and the software application prototype, performing simulation and analyzing the results, and making
recommendations for improvements. We have successfully employed such student-based and faculty-led
teams in many projects of similar scope and purpose in the past. Participant students will be majoring in
Computer Science or Electrical Engineering with likeliness of future professional involvement in the
electric power industry sector. Having Avista as a project sponsor would enhance student engagement and
performance and greatly benefit the student's careers. We are planning to hire the two graduate students
that worked on phase I of this project. They conducted phase I with enthusiasm gaining expertise in both
the power and web application software.
4. Rationale and Advantages
The adoption of consumer-level electric energy production and storage and of smart consumer
appliances is accelerating. Examples of technologies leading this rapid adoption are photovoltaics, small
wind, electric vehicles, and smart water heaters and freezers, among other technologies. Within such a new
prosumer-enabled Smart Grid, enabling the managed, timely, and selective trading and transmission and
distribution of electric power between prosumers (producer-consumer) and with the utility, with utility
oversight, may enable a more efficient use of the Power Grid as well as provide utility savings on generation
and transmission. For example, a temporary increase in demand from a smart-grid-enabled customer could
be served from a grid-near prosumer with stored capacity or current wind or solar generation.
The following is an example of how the proposed prosumer/consumer power trading may work. One
of Avista®’s customers installs solar panels or has a storage system such as an electric vehicle and generates
or stores electricity. To sell some of this customer’s excess electricity, this customer goes to an Avista®-
supervised website or app and enters the amount of energy that he wants to sell, the target price and
acceptable ranges, its renewable nature, and the restrictions and timing for the sharing of excess produced
power. Another Avista® customer needs some additional electricity at a given or scheduled time. Such
customer goes to the same website or app and indicates an interest in buying energy under certain
conditions, price ranges, and time period. The proposed system then creates a match between the prosumer
energy offers, requests, restrictions, timing, pricing ranges, and power grid status. Then informs the
customers, enables and records the business transaction, and enables scheduling of the transfer of power.
In a future implementation the system would also verify that the customers effectively traded the electric
power as scheduled and promised. All these transactions facilitated and controlled by Avista®. Avista®
provides the matching and transactions platform, the electrical path to exchange the energy, and the
transaction verification, and charges a fee for such services.
The proposed project will contribute to the concretization of the “transactive energy” vision where
power flows directions could be reversed (i.e., from customer to the grid) motivated by financial gains
through competitive pricing of electricity. The utility is paid for enabling the power flow through its
distribution and transmission systems. Avista® has interest in making this happen among many of its
customers. In fact, when one of this project’s PIs presented Avista® engineers with an idea that addressed
only the electrical issues of such a system, the engineers suggested adding the customer-initiated
transactions to the project.
5. Work Developed in Phase I
In phase I of this project, we completed the analysis and design and are working on the
implementation of an integrated prototype software system and power model that would enable Avista®’s
to manage prosumer-enabled transactions and also determine whether such transactions could be made
effective within a simulated small-scale power system given the requested transaction's timing and power
characteristics. The software components were constructed using leading modern technologies and
application development tools and the interfaces are Web-based. Data storage was implemented using
modern database technologies which would enable the use of modern analytics and business intelligence
tools. Also, in phase I, we studied the electrical behavior of small generation, such as home solar panels,
and the issues of electrical compatibility to the distribution network. We developed a model of a typical
electric grid in coordination with Avista engineers. We simulated the grid condition on an IEEE 13 bus
system with different load profiles and scheduled prosumer transactions.
6. Objectives of Phase II
The objectives of the proposed phase II are:
1 - Develop an algorithm for customer-initiated energy transaction prioritization and pricing.
2 - Enhance the transaction management application prototype to support
integration with the prioritization and pricing algorithm.
3 - Develop a richer or bigger evaluation grid model and scenarios.
4 - Test the prioritization algorithm and prototype system with the richer grid model.
7. Technical Approach
The proposed transaction prioritization and pricing algorithm would have the goal of maximizing the
economic benefit, welfare, and availability of the electric power network in the presence of prosumers while
adequately incentivizing prosumer-offered services where and when needed. Parameters that will be
considered and analyzed for integration within this prioritization and pricing algorithm include: wholesale
and consumer electricity rates, prosumer and consumer site capacity and target rates, timing of the requested
transactions, likeliness of the service being made effective, customer and prosumer and consumer site pre-
purchased or on-demand purchased priorities, proposed transaction sizes, potential generated power losses
and reverse flows, potential lines congestion, estimated voltage and reactive power support, and physical
and resiliency system constraints. The power system constraints will be integrated in the prioritization
algorithm that will consider the distribution locational marginal pricing (DLMPs) as proposed in the recent
literature. DLMPs defining the spot prices are more challenging than LMPs at the transmission level
because of the need to account for voltage limits, reactive power flows and losses. The impact of the
prototype prioritization and pricing algorithm will be evaluated and analyzed on one small and one medium
sized simulated distribution systems with a set of synthetically generated transaction requests. Services
necessary to transport the energy must be documented. For example, reactive power is necessary to stabilize
the voltage. If the public utility produces this, then a fee may be in order. The same is true for grid
management services. Methods to establish a fee structure for these services are certainly important, we
will investigate the latest developments. Finally, coordination of generation from renewables and loads with
the rest of the grid to ensure that the grid constraints and limits are respected is very important.
8. Potential Utility of Project Results
If this investigation is successful, it will provide concrete avenues for solving one of the most important
problems for incorporating prosumers as providers within the public utility system. Within this proposed
system, the price of shared power will be established by the seller, buyer, and AVISTA in a private
commercial transaction, negotiated by these parties. With many producers and consumers making these
transactions, the price gets established in the same manner as prices for commodities in competitive markets
have been determined for many centuries. As time permits, packaging and marketing this as software will
be investigated. After project end, further enhancements to the software prototype by Avista or a third
party will require purchasing of commercial licenses for the fourth-generation application development
tools. In the context of the work done so far, these products will likely consist of software and methods for
integrating photovoltaic assets into a system to reduce expenditures on additional distribution capacity into
areas on the periphery of a public utility’s system.
9. Specific Project Plan
The proposed tasks for the software effort are:
• T1: Meet with Avista® representatives to gather system requirements.
• T2: Review of the literature on prioritization and pricing strategies at the distribution level.
• T3: Evaluate options for revenue or objective prioritization and pricing functions.
• T4: Enhance the simulated model of the power distribution network developed in phase I.
• T5: Implement the prioritization algorithm in a modern programming language.
• T6: Integrate the transaction control system prototype with the updated distribution model.
• T7: Analyze the power grid condition and economic benefits for different scenarios.
• T8: Design and perform tests for: Functionality, Usability, and Security.
• T9: Produce user and administrator usage guides.
10. Proposed Project Schedule
18 August 2019 Project Kickoff. T1, T2, T3, T4 start.
01 December 2019 T1, T2, T3 complete. T5 starts.
01 January 2020 T4 complete.
01 March 2020 T6 and T7 start. T5 complete. Prototype refinement.
01 June 2020 T6 complete. T8 starts. Prototype refinement II.
01 August 2020 T7 complete. T9 starts. Final prototype refinement III.
31 August 2020 T9 complete and Final report.
11. Potential Market Path
This project will develop the technology to enable individuals entering the business of producing
and marketing power on a very small scale. Results from this project provide a path on how to proceed
with this technology for enabling the proposed trading market. Meanwhile, the software prototype could
be further evolved and tested by enlarging the simulations and small-scale pilot projects. Studies will
identify and address issues of software and electrical distribution to eventually create a useful product for
marketing electrical power. Engaging energy development resources, for example through US Department
of Energy or State of Idaho innovation and entrepreneurial funding could follow. Creating a business plan
would be appropriate at that point in the development process, when nearing completion of such follow-on
projects. At the pace that similar products have been developed, it is reasonable to estimate that a working
system may be available within five years. The savings to ratepayers and the utility will depend upon prices
that are negotiated and on the fee structure for public utility support services.
12. Criteria for Measuring Success
Success of this project will be measured by the on-time achievement of the proposed project
schedule and deliverables, including the software prototypes.
1. Written final report of the results of these studies in the format approved by Avista.
2. Interim reports and online conferences with Avista. Mid-term report.
3. Proof-of-concept software and user and administrative documentation.
4. Proof-of-concept evaluation within a small-scale simulated transmission and distribution system.
5. Outlines of proposals for follow-on funding to further develop and refine the prototype.
13. Budget Price Sheet and Budget Justification
Expense Category Year 1
A Project Director, Principal Investigators, and Prog. Manager Salaries $6,385
B Project Director, Principal Investigators, and Prog. Manager Fringe
Benefits
(http://www.uidaho.edu/osp/fringebenefitstable)
$2,022
C Graduate Students Researcher Salaries $35,547
D Graduate Student Researchers Fringe Benefits $1,554
E Supplies, Software, and Computing Equipment Rental $2,100
F Travel to Avista Headquarters in Spokane, WA $350
G Graduate Student Tuition and Health Insurance $24,084
H Modified Total Direct Cost (MTDC) (A+B+C+D+F) $47,958
I Facilities and Administrative Costs (50.30%)
(https://www.uidaho.edu/research/faculty/resources/f-and-a-rates)
$24,123
J Other Direct Costs (No F&A, Row G) $24,084
K Total Amount of Request (H+I+J) $96,164

Total Requested: $96,164: MTDC $47,958 + Student Supp. $24,084+ Indirect $24,123.
Senior Personnel Salaries and Fringe Benefits: $8,407: Salaries $6,385 + Fringe Benefits $2,022. Just:
Senior personnel roles are: architectural design, manage, direct, administer project, and mentor students.
Student Salaries and Fringe Benefits: $37,101: Salaries: $35,547+ Fringe benefits $1,554. Justification:
Two graduate students at $20.50/hour for 20 hours/week * 35 weeks during the academic year plus 15
hours/week * 10 weeks during Summer 2019. Student Tuition and Health Insurance: $24,084: Academic
year in-state tuition for graduate student $12,042; Summer health insurance is included. Tuition and health
insurance not subject to F&A.
Hardware and Software Services: $2,000: Cover costs of 4th generation application development tools
and server licenses plus rental of time and space of computing, networking, and storage from Center for
Secure and Dependable Systems or cloud provider Background check of each students and professor as
required by university policy. Travel: $350: Two trips to Avista in Spokane to present results and to consult
with Avista engineers and management.
14. Proposal Exceptions to this RFP
Per section 5.2 of the RFP, the University has described exceptions to RFP requirements and
conditions in the letter dated March 25, 2019, and included with Appendix A.
15. Appendix A: Proposal Cover Sheet
A completed and signed cover sheet is included as part of the RFP response from the U. Idaho.
16. Appendix C: Facilities and Equipment
This proposal if awarded will be carried out at the University of Idaho Campus in Moscow, ID.
The laboratories and facilities available to the proposed project are described here.
RADICL-Moscow: A Hands-On Instructional Computing Laboratory
The University of Idaho’s Cybersecurity Lab or RADICL is the “Reconfigurable Attack-Defend
Instructional Computing Laboratory.” The goal of this special purpose laboratory is to enable hands-on
teaching and research in the areas of cybersecurity, cyber-defense, and modern computing platforms and
networks. Since RADICL’s inception, its computing and software infrastructure has gone through several
improvements. The latest improvements, implemented in 2014, were funded by the State of Idaho under
the Idaho Global Entrepreneurial Mission (IGEM). The current configuration of RADICL makes full use
of virtualization features built into modern computing environments.
RADICL enables teams of students and researchers to create and deploy multiple independent
experiments that are quick to set-up and modify. Within the context of these isolated experiments,
students and researchers design, implement, examine, explore, and develop a detail-oriented and hands-on
view of modern computing infrastructures, along with their associated applications and protocols, and
their strengths, weaknesses, and vulnerabilities. In addition, in RADICL, students and researchers develop
a clear, detail-oriented, and hands-on understanding of the approaches, techniques, and tools used to
protect today’s computing systems and applications. RADICL also provides a dedicated and isolated
platform that enables students to prepare and practice for cyber defense competitions, such as the Pacific
Rim Collegiate Cyber Defense Competition (PR-CCDC) and the CSAW Capture the Flag Competition.
RADICL is a world-class and state-of-the-art computing laboratory that enables hands-on and
student-oriented instruction and hands-on graduate and undergraduate research. It is one of the bases for
the computer laboratory and classroom design in this proposal.
Power Applications Laboratory
The University of Idaho’s Power Applications Research Group facilities in Moscow include
educational and research laboratory facilities and office space for students.
The Power Applications Laboratory has a cyber-physical system test-bed centered on two real-
time digital simulator with a combination of commercial protection and control equipment, phasor
measurement units and SCADA equipment. This lab is currently being upgraded through a combination
of State of Idaho support and support from the Murdock Charitable Trust to provide the ability to simulate
much larger and more complex systems and can be used to simulate the power systems needed for this
project. In addition, the upgrade includes the capability to tie this laboratory to the RADICL
Cybersecurity laboratory and to the upcoming Cybersecurity Analytics and Visualization laboratory. In
addition, future connections to research facilities at the University of Idaho campuses in Idaho Falls and
Coeur d’Alene are planned.
The Power Applications Laboratory also includes an analog model power system that is capable
of simulating interaction of control and protection hardware in a network with up to five lines of up to
300 miles length that can be arbitrarily cut and connected. Our system protection hosts a full complement
of commercial protective relays and a fault generator capable of any type of common fault with any fault
impedance and any duration from balance of cycle to two weeks at a 50usec tolerance on fault initiation.
Multiple generation sources can be interfaced with the system including synchronous machines, a doubly
fed induction generator and power electronically coupled generation. Our laboratory floor in this lab has
1500 square feet of space for experiments.
The Power Applications Laboratory also includes an electric power laboratory with DC power
sources rated 125V / 250V DC at 400/200 Amps. Our AC is 120V, 240V three phase at 50kVA each. We
have three other individual DC generation sets at 120V, 100A each and two synchronous and three
induction machines at 10hp, each with its own dynamometer capability. Our five individual DC electronic
power supplies are 120V, 7A. We have a full complement of instruments to support measurements at
these levels. Our laboratory floor in this lab has 4681 square feet of available space in a main open bay
and three separate secure rooms to set up experiments. Available software tools include the following
general-purpose tools: Matlab, Mathcad, and LABView, in addition to power system specific software
tools such as Powerworld, DSATools, ATP, EMTP-RV, and PSCAD/EMTDC.
Center for Secure and Dependable Systems (CSDS)
The Idaho State Board of Education established the Center for Secure and Dependable Systems
(CSDS) at the University of Idaho in response to the overwhelming need for computer-related security
education and research. CSDS comprises faculty in the areas of Computer Science, Business, Electrical
and Computer Engineering, Civil Engineering, Mathematics, and Sociology, including associates at Idaho
National Laboratory (INL) and Pacific Northwest National Laboratory (PNNL), over 30 students, and
3,000 square feet of laboratory and office space. It is housed in the Janssen Engineering Building, on the
Moscow, Idaho campus, faculty offices are not included in the space totals for CSDS.
Completely self-funded, CSDS brings together collaborative research and efforts and serves as an
educational focal point for the design, development, analysis, and use of technologies that result in secure
and dependable computing systems and networks. CSDS is also a leader in Information Assurance and
Cyber Defense education in the Northwest.
The University of Idaho College of Engineering
The University of Idaho's College of Engineering is composed of 6 academic departments and 5
research and development centers. The college has about 200 faculty and staff and a student body of 1500
undergraduate student and 350 graduate students. The College of Engineering has three full-time
dedicated Information Technology personnel and one full-time Information Technology system
administrator dedicated exclusively to manage research infrastructure. Our research infrastructure
includes many fully virtualized modern servers, large storage arrays, a supercomputer, and supporting
high-speed fiber-based network infrastructure, among other specialized computing equipment. Computing
services and software tools provided by the Center for Secure and Dependable Systems and the College of
Engineering will be rented to ensure the availability of modern equipment, platforms, and tool for the
implementation of this project.
The University of Idaho Library
The University of Idaho library houses over a million books and almost ten thousand periodical
subscriptions, in print and online. It has served for over a century as an official regional depository of
U.S. federal government publications, making almost two million government documents available to the
public. The library’s Special Collections are an invaluable resource for researchers, providing access to
historical photographs, state documents, university historical materials, rare books, digital collections, and
the International Jazz Collections, the premiere jazz archives of the Pacific Northwest. All library
resources are available to all students and faculty.

17. Appendix D: Biographical Sketches
Biographical Sketch
Yacine Chakhchoukh, Ph.D.
Assistant Professor of Electrical Engineering
University of Idaho, GJL 213, Moscow, Idaho 83844-1023
Phone: [phone]; Email: [e-mail]

Professional Preparation
National Polytechnic School of Algiers, Algeria Electrical Engineering BSEE, 2004.
University of Paris-Sud XI, Paris, France Electrical Engineering MSEE, 2005.
University of Paris-Sud XI, Paris, France Electrical Engineering PhD, 2010.
Appointments
2016-present: Assistant Professor, Electrical Engineering, University of Idaho.
2015–2016: Project Assistant Prof., Electrical Eng., Tokyo Institute of Technology, Japan.
2013-2015: Postdoctoral Fellow, Electrical Eng., Tokyo Institute of Technology, Japan.
2011-2013: Postdoctoral Fellow, Electrical Engineering, Arizona State University, AZ, USA.
2009–2011: Postdoctoral Fellow, Electrical Eng., Technical University Darmstadt, Germany.
2006–2009: Research Engineer, French Transmission System Operator, RTE-France.
Products (five related to this proposal)
01. Y. Chakhchoukh, V. Vittal, G. T. Heydt and H. Ishii, “LTS-based Robust Hybrid SE Integrating
Correlation”, IEEE Transactions on Power Systems, Vol. 32, No. 4, pp. 3127-3135, July 2017.
02. Y. Chakhchoukh and H. Ishii, “Enhancing Robustness to Cyber-Attacks in Power Systems Through
Multiple Least Trimmed Squares State Estimations,” IEEE Transactions on Power Systems, Vol. 31,
No. 6, pp. 4395-4405, Nov. 2016.
03. Y. Chakhchoukh and H. Ishii, “Coordinated Cyber-Attacks on the Measurement Function in Hybrid
State Estimation,” IEEE Transactions on Power Systems, Vol. 30, No. 5, pp. 2487-2497, Sept. 2015.
04. Y. Chakhchoukh, P. Panciatici and L. Mili, “Electric load forecasting based on statistical robust
methods”, IEEE Transactions on Power Systems, Vol. 26, No. 3, pp. 982-991, Aug. 2011.
05. A. M. Zoubir, V. Koivunen, Y. Chakhchoukh and M. Muma, "Robust Estimation in Signal
Processing: A Tutorial-Style Treatment of Fundamental Concepts," IEEE Signal Processing
Magazine, Vol. 29, No. 4, pp. 61-80, July 2012. Best paper award in 2017.

Products (five other significant)
01. Y. Chakhchoukh, V. Vittal and G. Heydt, “PMU based State Estimation by Integrating correlation”,
IEEE Transactions on Power Systems, Vol. 29, No. 2, pp. 617-626, March 2014.
02. J. Quintero, H. Zhang, Y. Chakhchoukh, V. Vittal and G. Heydt, “Next Generation Transmission
Expansion Planning Framework: Models, Tools, And Educational Opportunities”, IEEE Transactions
on Power Systems, Vol. 29, No. 4, pp. 1911-1918, July 2014.
03. Y. Chakhchoukh, S. Liu, M. Sugiyama and H. Ishii, “Statistical Outlier Detection for Diagnosis of
Cyber Attacks in Power State Estimation”, Proceedings of the 2016 IEEE Power and Energy Society
General Meeting, Boston, MA, July 17-21, 2016.
04. V. Murugessen, Y. Chakhchoukh, V. Vittal, G. T. Heydt, N. Logic and S. Sturgill, “PMU data
Buffering for Power System State Estimators”, IEEE Power and Energy Technology Systems Journal,
Vol. 2, No. 3, pp. 94-102, Sep. 2015.
05. Q. Zhang, Y. Chakhchoukh, V. Vittal, G. Heydt, N. Logic and S. Sturgill, “Impact of PMU
Measurement Buffer Length on State Estimation and its Optimization,” IEEE Transactions on Power
Systems, Vol. 28, No. 2, pp. 1657-1665, May 2013.
Synergistic Activities
1. IEEE Power and Energy Society (PES) Member
2. Chair of the panel session: “Addressing Uncertainty, Data Quality and Accuracy in State Estimation” at
the 2018 IEEE General meeting: http://pes-gm.org/2018/
3. Reviewer for several journal and conferences in power systems, smart grid, signal processing and
control theory.

Biographical Sketch
Daniel Conte de Leon, PhD.
Assistant Professor of Computer Science and Cybersecurity,
Center for Secure and Dependable Systems and Computer Science Department,
University of Idaho, JEB 233, Moscow, Idaho, 83844-1010, U.S.A.
Phone [phone]; Email: [e-mail]
Professional Preparation
UCUDAL, Montevideo, Uruguay, Major: CS, Degree: Informatic Systems Analyst, Year: 1998.
Univ. of Idaho, Moscow, Idaho, Major: Computer Science, Degree: Masters of Sci., Year: 2002.
Univ. of Idaho, Moscow, Idaho, Major: Computer Science, Degree: Doctor of Phil., Year: 2006.
Appointments
2019-Aug.-Present: Associate Professor of Computer Science, University of Idaho (UI).
2013-2019: Assistant Professor of Computer Science, University of Idaho (UI).
2007-2013: Associate Professor of Computer Science, Lewis-Clark State College.
Selected Publications
01. Oyewumi, Ibukun A.*; Jillepalli, Ananth A.*; Richardson, Phillip*; Ashrafuzzaman, Mohammad*;
Johnson, Brian K.; Chakhchoukh, Yacine; Haney, Michael A.; Sheldon, Frederick T.; Conte de Leon,
Daniel;, “ISAAC: The Idaho CPS Smart Grid Cybersecurity Testbed,” Proceedings of the 3rd IEEE
Texas Power and Energy Conference (TPEC-2019), (IEEE), Feb. 2019. DOI: https:
//doi.org/10.1109/TPEC.2019.8662189.
02. Jillepalli, Ananth A.*; Conte de Leon, Daniel; Oyewumi, Ibukun A.*; Alves-Foss, James; Johnson,
Brian K.; Jeffery, Clint L.; Chakhchoukh, Yacine; Haney, Michael A.; Sheldon, Frederick T.,
“Formalizing an Automated, Adversary-aware Risk Assessment Process for Critical Infrastructure,”
Proceedings of the 3rd IEEE Texas Power and Energy Conference (TPEC-2019), (IEEE), Feb. 2019.
DOI: https://doi.org/10.1109/TPEC.2019.8662167.
03. Jillepalli, Ananth A.; Conte de Leon, Daniel; Chakhchoukh, Yacine; Ashrafuzzaman, Mohammad;
Johnson, Brian K.; Sheldon, Frederick T.; Alves-Foss, Jim; Tosic, Predrag T.; Haney, Michael A.
"An architecture for HESTIA: High-level and Extensible System for Training and Infrastructure Risk
Assessment," International Journal of Internet of Things and Cyber-Assurance, Indersience, 2018.
04. Conte de Leon, Daniel; Goes, Christopher E.; Jillepalli, Ananth A.; Haney, Michael A.; Krings, Axel.
"ADLES: Specifying, Deploying, and Sharing Hands-On Cyber-Exercises", Computers and
Security (C&S-Elsevier), 2018. License: CC-BY. DOI: https://doi.org/10.1016/j.cose.2017.12.007.
Link: https://www.sciencedirect.com/science/article/pii/S0167404817302742.
05. Conte de Leon, Daniel; Stalick, Antonius Q.; Jillepalli, Ananth A.; Haney, Michael A.; Sheldon,
Frederick T. "Blockchain: Properties and Misconceptions", Asia Pacific Journal of Innovation and
Entrepreneurship, Vol: 11 Issue: 3, pp. 286-300, December 2017. CC-BY. DOI: https://doi.org/
10.1108/APJIE-12-2017-034. https://www.emeraldinsight.com/doi/abs/10.1108/APJIE-12-2017-034.
06. Conte de Leon, Daniel; Brown, Matthew G.; Jillepalli, Ananth A.; Stalick, Antonius Q.; Alves-Foss,
Jim. "High Level and Formal Router Policy Verification," The Journal of Computing Sciences in
Colleges, Volume 33, Number 1, pp. 118, October 2017. CCSC and ACM 2017. DOI: None. Link:
https://dl.acm.org/citation.cfm?id=3144631.
07. Ananth A. Jillepalli, Daniel Conte de Leon, Stuart Steiner, and Frederick Sheldon, “HERMES: A
High-Level Policy Language for High-Granularity Enterprise-wide Secure Browser Configuration
Management,” Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence
(SSCI-2016), 06-09 December 2016, Athens, Greece, IEEE Computer Society, 2016.
http://dx.doi.org/10.1109/SSCI.2016.TBD
08. Ananth A. Jillepalli and Daniel Conte de Leon, “An Architecture for a Policy-Oriented Web Browser
Management System: HiFiPol: Browser,” Proceedings of the 40th Annual IEEE Computer
Software and Applications Conference (COMPSAC-2016), June 2016, Atlanta, GA, U.S.A. IEEE
Computer Society, 2016. http://dx.doi.org/10.1109/COMPSAC.2016.50
09. Daniel Conte de Leon and Jim Alves-Foss, “Hidden Implementation Dependencies in High Assurance
and Critical Computer Systems,” IEEE Transactions on Software Engineering (IEEE-TSE),
Volume 32, Number 10, October 2006, pages 342-349, IEEE Computer Society, Los Alamitos, CA,
U.S.A. http://dx.doi.org/10.1109/TSE.2006.103
10. Paul W. Oman, Axel Krings, Daniel Conte de Leon, and Jim Alves-Foss, “Analyzing the Security and
Survivability of Real-time Control Systems,” Proceedings of the 5th Annual IEEE Information
Assurance Workshop (IAW’04), 10-11 June 2004, U.S. Military Academy, West Point, NY, U.S.A.
IEEE Computer Society, 2004. http://dx.doi.org/10.1109/IAW.2004.1437837
11. Ananth A. Jillepalli and Daniel Conte de Leon and Sanjeev Shrestha, “Requirements are the New
Code,” Proceedings of the 40th Annual IEEE Computer Software and Applications Conference
(COMPSAC-2016), June 2016, Atlanta, GA, U.S.A. IEEE Computer Society, 2016.
http://dx.doi.org/10.1109/COMPSAC.2016.265
12. Luay A. Whasheh, Daniel Conte de Leon, and Jim Alves-Foss, “Formal Verification and
Visualization of Security Policies,” Journal of Computers (JCP), Volume 3, Issue 6, June 2008,
Academy Publisher, Oulu, Finland. http://academypublisher.com/jcp/vol03/no06/jcp03062231.html
13. Daniel Conte de Leon, Jim Alves-Foss, and Paul W. Oman, “Implementation-Oriented Secure
Architectures,” Proceedings of the 40th Hawaii International Conference on System Sciences
(HICSS-40), 03-06 January 2007, Big Island, HI, U.S.A. IEEE Computer Society, 2007.
http://dx.doi.org/10.1109/HICSS.2007.264.
14. Daniel Conte de Leon and Jim Alves-Foss, “Experiments on Processing and Linking Semantically
Augmented Requirement Specifications,” Proceedings of the 37th Hawaii International
Conference on System Sciences (HICSS-37), 05-08 January 2004, Big Island, HI, U.S.A. IEEE
Computer Society, 2004. http://dx.doi.org/10.1109/HICSS.2004.1265657
15. Jim Alves-Foss, Daniel Conte de Leon, and Paul. W. Oman, “Experiments in the Use of XML to
Enhance Traceability between Object-Oriented Design Specifications and Source Code,”
Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS-35), 05-08
January 2002, HI, U.S.A. IEEE, 2002. Cited by 3 U.S. Patents.
http://dx.doi.org/10.1109/HICSS.2002.994466
Synergistic Activities
1. ISAAC: Idaho Industrial Control Systems Cybersecurity Testbed: I collaborate on the design and
implementation of a state-wide testbed for cybersecurity research. When completed this testbed will
connect five laboratories at the University of Idaho including Power Lab, Visualization and Analytics,
Cybersecurity, Industrial Control Cybersecurity, and IoT Labs. This testbed will enable world-class
research on power, ICS, and cybersecurity including adversarial and attack-defend scenarios.
2. Hands-On Cybersecurity Tutorials: I lead the development and publication of complete and self-
contained Hands-On Tutorials for Cybersecurity Education.
3. ACM/IEEE Computer Science Curricula 2013: I participated in the development of the ACM/IEEE
Comp. Sci. Curricula 2013. Available: https://www.acm.org/education/CS2013-final-report.pdf
4. IEEE Standards Association Voting Member: I have carefully reviewed and voted on more than 10
IEEE standards. Two examples are: “ISO/IEC/IEEE Systems and Software Engineering -
Architecture Description” and “IEEE Draft Recommended Practice for the Use of Probability
Methods for Conducting a Reliability Analysis of Industrial and Commercial Power Systems.”
5. Hands-On Instructional Computing Laboratory: I manage the Reconfigurable Attack-Defend
Instructional Computing Laboratory (RADICL-Moscow). RADICL is a specialized computing
laboratory that enables hands-on teaching and research in cybersecurity.
Biographical Sketch
Herbert L. Hess
Professor of Electrical Engineering
University of Idaho, GJL 205, Moscow, Idaho 83844-1023
Phone: [phone]; Email: [e-mail]
Education
Ph.D., Electrical and Computer Engineering, Univ. of Wisconsin-Madison, 22 August 1993.
S.M., Electrical and Computer Engineering, Mass. Institute of Technology, 15 September 1982.
B.S., Applied Science and Engineering, United States Military Academy, 8 June 1977.
Experience
2006-Present: Professor, University of Idaho.
1999-2006: Associate Professor, University of Idaho.
1993-1999: Assistant Professor, University of Idaho.
2001-2005: Reserve Research Engineer, US Army RDECOM.
2001-2002: Electrical Engineer, US Army RDECOM.
1989-2000: Reserve Professor, United States Military Academy.
1983-1988: Assistant Professor, United States Military Academy.
Research Interests
Power electronic converters, great and small: on-chip architectures for switching power electronic
converters and their constituent transistors, motor drives, power supplies, battery chargers and
monitors, large switching power converters, power quality.
Professional Membership
IEEE (Societies: IES, IAS, PELS, PES, EDS)
ASEE (Divisions: ECE, ECCD, Instrumentation).
The Honor Society of Phi Kappa Phi (University of Idaho Chapter Past President).

Publications and Patents
01. Wiegers, R.*, D. Blackketter, and H. Hess, “A Method for Balancing Ultracapacitor Voltage Arrays
in an Electric Vehicle Braking System,” International Journal of Vehicle Design, accepted for
publication.
02. Samineni, S.*, B. Johnson, H. Hess, and J. Law, “Modeling and Analysis of a Flywheel Energy
Storage System for Voltage Sag Correction, IEEE Transactions on Industry Applications, XLII, 1,
January/February 2006, pp. 1-11.
03. Martinez, J., B. Johnson, and H. Hess, “Power Semiconductors,” IEEE Transactions on Power
Delivery, XX, 3, July 2005, pp. 2086-2094.
04. Alahmad, M.*, M. Braley*, J. Nance*, V. Sukumar*, K. Buck*, H. Hess, and H. Li, “Microprocessor
Based Battery Power Management System Enhances Charging, Monitoring, and Protection Features,”
Battery Power Products and Technology, VIII, 6, November 2004, pp. 17-19.
05. Muljadi, E, H.L. Hess, and K. Thomas*, “Zero Sequence Method for Energy Recovery from a
Variable- Speed Wind Turbine Generator,” IEEE Transactions on Energy Conversion, XVI, 1, March
2001, pp. 99-103.
06. Johnson, B.K., and H.L. Hess. “Active Damping for Electromagnetic Transients in Superconducting
Systems.” IEEE Transactions on Applied Superconductivity, IX, 6, June 1999, pp. 318-321.
07. Hess, H.L., D.M. Divan, and Y.H. Xue*. “Modulation Strategies for a New SCR-Based Induction
Motor Drive System with a Wide Speed Range.” IEEE Transactions on Industry Applications, XXX,
6, November-December 1994, pp. 1156-1163.
08. Umans, S.D., and H.L. Hess. “Modeling and Analysis of the Wanlass Three Phase Motor
Configuration.” IEEE Transactions on Power Apparatus and Systems, CII, 9, September 1983, pp.
2912-2921.
09. Padaca, V.F., and H. Hess. “Voltage Sags Plague a Food Processing Facility.” Power Quality
Assurance, VII, 1, January-February 1997, pp. 1-5 (invited technical article).
10. Peterson, J.N., and HL Hess, “Feasibility, Design and Construction of a Small Hydroelectric Power
Generation Station as a Student Design Project," American Society for Engineering Education 1999
Annual Conference, July 1999, Session 2633. Best Paper Overall Conference.
11. Mentze, E.*, K. Buck*, H. Hess, D. Cox, H. Li, and M. Mojarradi, Patent Pending, “High Voltage to
Low Voltage Level Shifter,” US Patent #7,061,298, 13 June 2006.
12. Hess, H.L., and D.M. Divan, “Thyristor Based DC Link Current Source Power Conversion System for
Motor Driven Operation,” U.S. Patent 5,483,140, 09 January 1996.
Biographical Sketch
Brian K. Johnson, Ph.D., P.E.
Professor of Electrical Engineering
Schweitzer Engineering Laboratories Endowed Chair in Power Engineering
University of Idaho, GJL 201, Moscow, Idaho 83844-1023
Phone: [phone]; Email: [e-mail]
Professional Preparation
University of Wisconsin-Madison, Madison, WI Electrical Engineering BSEE, 1987.
University of Wisconsin-Madison, Madison, WI Electrical Engineering MSEE, 1989.
University of Wisconsin-Madison, Madison, WI Electrical Engineering PhD, 1992.
Appointments
2004–present: Professor Electrical Engineering, University of Idaho.
2006-2012: Chair, Department of Electrical and Computer Engineering.
1997–2004: Associate Professor, Electrical Engineering, University of Idaho.
1992–1997: Assistant Professor, Electrical Engineering, University of Idaho.
Professional Registration
Registered Professional Engineer (Idaho #8368)
Recent Publications
01. Taylor, D.I., J.D. Law, B.K. Johnson, and N. Fischer. “Single-Phase Transformer Inrush Current
Reduction Using Prefluxing,” IEEE Transactions on Power Delivery, Vol. 27, No. 1, January 2012,
pp. 245-252.
02. K. Eshghi, B.K. Johnson, C.G. Rieger, “Power System Protection and Resilient Metrics” Proceedings
of the 2015 Resilience Week, Philadelphia, PA, August 18-20, 2015.
03. R. Jain, B. Johnson, H. Hess, “Performance of Line Protection and Supervisory Elements for Doubly
Fed Wind Turbines” Proceedings of the 2015 IEEE Power and Energy Society General Meeting,
Denver, Colorado, July 27-31, 2015.
04. A. Guzmán, V. Skendzic, M. V. Mynam, S. Marx, B. K. Johnson, “Traveling Wave Fault Location
Experience at Bonneville Power Administration,” Proceedings of the International Conference on
Power Systems Transients (IPST2015), Dubrovnik, Croatia, July 15-18, 2015.
06. B. K. Johnson, S. Jadid, “Synchrophasors for Validation of Distance Relay Settings: Real Time
Digital Simulation and Field Results,” Proceedings of the International Conference on Power Systems
Transients (IPST2015), Dubrovnik, Croatia, July 15-18, 2015.
07. H. Li, G. Parker, B.K. Johnson, J.D. Law, K. Morse, D.F. Elger, “Modeling and Simulation of a High-
Head Pumped Hydro System,” 2014 IEEE Transmission and Distribution Conf. & Expo, April 2014.
08. Y. Xia, B.K. Johnson, H. Xia, N. Fischer, “Application of Modern Techniques for Detecting
Subsynchronous Oscillations in Power Systems.” Proceedings of the 2013 IEEE Power and Energy
Society General Meeting, Vancouver Canada, July 21-25, 2013.
09. Y. Xia, B.K. Johnson, N. Fischer, H. Xia, “A Comparison of Different Signal Selection Options and
Signal Processing Techniques for Subsynchronous Resonance Detection,” Proceedings of the
International Conf. on Power Systems Transients (IPST2013), Vancouver, Canada July 1820, 2013.
10. M.P. Bahrman and B.K. Johnson, “The ABCs of HVDC Transmission Technologies,” IEEE Power
and Energy. Vol. 5, No. 2, pp. 32-44, March-April 2007.
Related Research Projects
01. B.K. Johnson and J. Alves-Foss, “TWC: Small: Securing Smart Power Grids Under Data
Measurement Cyber Threats”, Syracuse University (subcontract of NSF funding). August 16, 2015-
August 15, 2018, $210,696.
02. B.K. Johnson and H.L. Hess, “Smart Wires for Increasing Transmission and Distribution Efficiency,”
Avista Corporation, August 23, 2015 – August 22, 2016, $75,044.
03. H.L. Hess and B.K. Johnson, “Critical Load Serving Capability by Optimizing Microgrid Operation,”
Avista Corporation, Oct 1-2015 – Sept 30, 2016, $79,856.
04. B.K. Johnson, “Online Synchronous Machine Parameter Identification,” Schweitzer Engineering
Laboratories, Inc. August 1, 2014-July 31, 2016, $155,037.
05. B.K. Johnson and H.L. Hess, “Modeling and Design Options for an All Superconducting Shipboard
Electric Power Architecture,” Office of Naval Research, October 2013-September, 2015, $56,894
06. Johnson, B.K, J.D. Law, and D.F. Elger, “Renewable Energy Balancing,” Shell Energy North
America, June 11, 2012-March 31, 2013, $75,000.
07. Johnson, B.K. and J.D. Law. “Subsynchronous Resonance Risk Assessment and Countermeasures,”
Laboratory for Applied Scientific Research (subcontract from Schweitzer Engineering Laboratories,
Inc.), March 31, 2012-January 31, 2013, $35,881.
08. Johnson, B.K. and Hess, H.L, “Modeling of Harmonic Injections and Their Impacts,” Idaho Power
Corporation, $48,674, June 1, 2006-August 15, 2007.
Biographical Sketch
Hangtian Lei, Ph.D.
Assistant Professor of Electrical Engineering
Schweitzer Engineering Laboratories Endowed Chair in Power Engineering
University of Idaho, GJL 211, Moscow, Idaho 83844-1023
Phone: [phone]; Email: [e-mail]
Professional Preparation
Huazhong University of Science & Technology, Wuhan, China, Electrical Eng., B.E., 2011.
Texas A&M University, College Station, Texas, Electrical Engineering, Ph.D. 2016.
Appointments
2017-Present: Assistant Professor, Electrical and Computer Eng., University of Idaho.
2016-2017: Assistant Professor, Electrical and Computer Engineering, Jackson State University.
2012-2016: Research Assistant, Electrical and Computer Engineering, Texas A&M University.
Publications
01. H. Lei and C. Singh, “Non-sequential Monte Carlo simulation for cyber-induced dependent failures in
composite power system reliability evaluation,” IEEE Transactions on Power Systems, vol. 32, no. 2,
pp. 1064-1072, March 2017, DOI: 10.1109/TPWRS.2016.2572159.
02. C. Singh, A. Sprintson, H. Lei, A. Heidarzadeh, V. Aravinthan, M. H. Kapourchali, and M. Sepehry,
“Reliability assessment and modeling of cyber enabled power systems with renewable sources and
energy storage (T-53),” Final Report to the Power Systems Engineering Research Center (PSERC),
November 2016, URL: http://pserc.wisc.edu/research/public_reports.aspx.
03. H. Lei and C. Singh, “Power system reliability evaluation considering cyber-malfunctions in
substations,” Electric Power Systems Research, vol. 129, pp. 160-169, December 2015, DOI:
10.1016/j.epsr.2015.08.010.
04. H. Lei, C. Singh, and A. Sprintson, “Reliability modeling and analysis of IEC 61850 based substation
protection systems,” IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2194-2202, September 2014,
DOI: 10.1109/TSG.2014.2314616.
05. H. Lei and C. Singh, “Developing a benchmark test system for electric power grid cyber-physical
reliability studies,” in Proc. the 2016 International Conference on Probabilistic Methods Applied to
Power Systems (PMAPS 2016), October 2016, pp. 1-5, DOI: 10.1109/PMAPS.2016.7764053.
06. H. Lei and C. Singh, “Incorporating protection systems into composite power system reliability
assessment,” in Proc. IEEE Power and Energy Society General Meeting, July 2015, pp. 1-5, DOI:
10.1109/PESGM.2015.7285636.
07. T. Balachandran, M. H. Kapourchali, M. Sephary, V. Aravinthan, M. Ni, S. Tindemans, H. Lei, et al.,
“Reliability Modeling Considerations for Emerging Cyber-Physical Power Systems,” the 2018
International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2018), June
2018, accepted for publication.
08. H. Lei, B. Chen, K. L. Butler-Purry, and C. Singh, “Security and reliability perspectives in cyber-
physical power systems,” the International Conference on Innovative Smart Grid Technologies, May
2018, accepted for publication.
09. C. Singh and H. Lei, “Cyberfication and its impact on power grid reliability,” the 9th IEEE PES Asia-
Pacific Power and Energy Engineering Conference 2017, November 2017, pp. 1-5, DOI:
10.1109/APPEEC.2017.8308941.
10. H. Lei, C. Singh, and A. Sprintson, “Reliability analysis of modern substations considering cyber link
failures,” in Proc. IEEE Power and Energy Society Innovative Smart Grid Technologies 2015 Asian
Conference, November 2015, pp. 1-5, DOI: 10.1109/ISGT-Asia.2015.7387031.
Synergistic Activities
01. June 2016 – July 2017: Principal Investigator (PI) of a university-industry research grant “Open Loop
Testing for Power System Electromagnetic Transient Studies,” Amount: $75,000. This research
project was funded by Entergy, the major electricity provider for Arkansas, Mississippi, Louisiana,
and southeast Texas areas. The objective of this project is to help Entergy better identify protection
system misoperations due to various causes (such as load change, surge arrester failure, and weather
change) by performing electromagnetic transient modeling and hardware-in-the-loop testing.
02. February 2016 – Present: Member of the IEEE Reliability, Risk, and Probability Applications (RRPA)
Subcommittee Task Force on the Reliability of Cyber-Physical Power Systems.
03. February 2016 – May 2017: Developed the syllabuses of two undergraduate courses for the
Department of Electrical and Computer Engineering at Jackson State University: (1) Power System
Analysis and (2) Electric Machines.
04. September 2016 – May 2017: Advised 10 undergraduate students in senior design projects at Jackson
State University.
05. February 2015 – Present: Technical Reviewer for IEEE Transactions on Power Systems, IEEE
Transactions on Smart Grid, and IEEE Communications Letters.
APPENDIX D
Final Report: IR Camera Phase II

Report Number: 2020_08_31

USING IR CAMERAS IN BUILDING CONTROLS – PHASE II
PROJECT REPORT PREPARED FOR AVISTA UTILITIES

November 30, 2020

Prepared for:
Avista Utilities

Authors:
Damon Woods
Jubin Mathai
Ken Baker

This page left intentionally blank.
Prepared by:
University of Idaho Integrated Design Lab | Boise
322 E Front St., Suite 360, Boise, ID 83702 USA
www.uidaho.edu/idl

IDL Director:
Ken Baker

Authors:
Damon Woods
Jubin Mathai
Ken Baker

Prepared for:
Avista Utilities

Contract Number: R-39872

Please cite this report as follows: Woods, D., Mathai, J., Baker, K.
(2020). Using IR Cameras in Building Controls – Phase II.
(2020_08_31). University of Idaho Integrated Design Lab, Boise,
ID.
DISCLAIMER
While the recommendations in this report have been reviewed for
technical accuracy and are believed to be reasonably accurate,
the findings are estimates and actual results may vary. All energy
savings and cost estimates included in the report are for
informational purposes only and are not to be construed as
design documents or as guarantees of energy or cost savings. The
user of this report, or any information contained in this report,
should independently evaluate any information, advice, or
direction provided in this report.
THE UNIVERSITY OF IDAHO MAKES NO REPRESENTATIONS,
EXTENDS NO WARRANTIES OF ANY KIND, EITHER EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE
WITH RESPECT TO THE INFORMATION, INCLUDING BUT NOT
LIMITED TO ANY RECOMMENDATIONS OR FINDINGS, CONTAINED
IN THIS REPORT. THE UNIVERSITY ADDITIONALLY DISCLAIMS ALL
OBLIGATIONS AND LIABILITIES ON THE PART OF UNIVERSITY FOR
DAMAGES, INCLUDING, BUT NOT LIMITED TO, DIRECT, INDIRECT,
SPECIAL AND CONSEQUENTIAL DAMAGES, ATTORNEYS’ AND
EXPERTS’ FEES AND COURT COSTS (EVEN IF THE UNIVERSITY HAS
BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES, FEES OR
COSTS), ARISING OUT OF OR IN CONNECTION WITH THE
MANUFACTURE, USE OR SALE OF THE INFORMATION, RESULT(S),
PRODUCT(S), SERVICE(S) AND PROCESSES PROVIDED BY THE
UNIVERSITY. THE USER ASSUMES ALL RESPONSIBILITY AND
LIABILITY FOR LOSS OR DAMAGE CAUSED BY THE USE, SALE, OR
OTHER DISPOSITION BY THE USER OF PRODUCT(S), SERVICE(S), OR
(PROCESSES) INCORPORATING OR MADE BY USE OF THIS REPORT,
INCLUDING BUT NOT LIMITED TO DAMAGES OF ANY KIND IN
CONNECTION WITH THIS REPORT OR THE INSTALLATION OF
RECOMMENDED MEASURES CONTAINED HEREIN.

This page left intentionally blank.

TABLE OF CONTENTS
1. Acknowledgements ..................................................................................................................... 3
2. Executive Summary ..................................................................................................................... 3
3. Research Motivation ................................................................................................................... 4
4. Project Methods ......................................................................................................................... 5
4.1 Downsizing the Camera ......................................................................................................... 5
4.2 Enhancing Occupant detection ............................................................................................. 7
5. Commercialization ...................................................................................................................... 9
5.1 Market ................................................................................................................................... 9
5.2 Competition ......................................................................................................................... 10
5.3 Future Funding and Development ...................................................................................... 11
6. Discussion .................................................................................................................................. 12
7. Budget Summary ....................................................................................................................... 13

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ACRONYMS AND ABBREVIATIONS
AHU Air Handling Unit
ASHRAE American Society of Heating Refrigeration and Air conditioning Engineers
EMS Energy Management Control System
EPW Energy Plus Weather file
HVAC Heating, Ventilation and Air Conditioning
IDL Integrated Design Lab
MRT Mean Radiant Temperature
OTT Office of Technology Transfer
PMV Predicted Mean Vote
PNNL Pacific Northwest National Laboratory
PPD Percentage of Population Dissatisfied
TMY Typical Meteorological Year
UI University of Idaho

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1. ACKNOWLEDGEMENTS
This research was made possible through funding support from Avista Utilities through the
Advanced Energy Research Initiative via Idaho PUC Case Number AVU-E-13-08. The research team is
very grateful for the project management from Natasha Jostad and JR Norvell at T-O Engineers. Thank
you to Randy Gnaedinger and Carlos Limon at Avista for their supervision, guidance, and support of this
project.
2. EXECUTIVE SUMMARY
The University of Idaho – Integrated Design Lab (UI-IDL) worked in 2019 and 2020 to advance the
infrared thermostat technology towards commercialization. The infrared thermostat combines a
miniature infrared camera on a microprocessing board to replace a traditional thermostat and deliver
more efficient heating and cooling signals. During Phase I of the project, the team set up a hand-held
infrared camera on a tripod in an experimental chamber to show the proof of concept. For this current
phase, the team worked to miniaturize and automate the camera. The team purchased a micro-infrared
camera, microprocessing board and motorized camera mount. We combined what had been separate
commands into one Python code that synchronizes the camera movement with image capture to estimate
comfort conditions and occupancy. We set up a WiFi network to link the camera to a thermostat so we
could send wireless commands between the two. The thermostat controlled a heater for a full ‘closed
loop’ test of the technology. We used our progress and our prototype to craft an initial business plan and
we secured commercialization funding from the M.J. Murdock Charitable Trust.

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3. RESEARCH MOTIVATION
Controlling a building based on both surface and air temperatures could reduce energy
consumption and promote healthier buildings. Most buildings manage their heating or cooling based
solely on zone air temperature. Even when the thermostats are kept within a narrow band of 4oF,
occupant complaints persist [1]. A large factor of human comfort is being missed in conventional
thermostats: the inclusion of the mean radiant temperature (MRT). The MRT is an average of the
surrounding surface temperatures. Many previous studies have established that the temperatures of the
surfaces surrounding us have a far greater impact on our comfort than the air temperature [2]. While
operative temperature is widely used for predicting human comfort, rarely is it directly used in building
controls.
Building operations could benefit greatly from operative temperature control. Operative
temperature control would allow for wider air temperature setpoints, thus saving energy each year. A
study by the Pacific Northwest National Lab estimated the following general energy savings: a 2oF
adjustment on both heating and cooling setpoints led to a fairly uniform HVAC energy savings of 12-20%
[3]. Nationwide, such savings would be equivalent to 370-615 trillion Btu saved annually [4]. This research
focus is decidedly narrower in scope: focusing purely on small commercial office buildings in the Pacific
Northwest, where HVAC energy is typically 35% of the annual load [4]. Research has shown that a
thermostat reset could result in total annual energy savings of 4-7% per building [5]. The adoption of wider
setpoints based on operative temperatures by controls engineers and consultants could quickly scale up
and result in widespread savings. In addition to energy savings, the operative temperature control could
dramatically improve occupant comfort, thus making it an attractive service to offer to clients.

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4. PROJECT METHODS
4.1 Downsizing the Camera
In 2019, the team had built a method for using an infrared camera to control a space based on
the ASHRAE 55 holistic comfort guidelines. The initial prototype included a 5”x2” camera mounted on a
tripod that could be manually adjusted to capture different angles in the room.

Figure 1: The original proof of concept with the Flir C2 camera mounted on a manual tripod
Each image captured was individually exported to a computer. Each time a photo was taken, a
researcher would manually process the data. In Phase 2 of the project, our team miniaturized and
automated the process. The IDL purchased a Flir Lepton camera about the size of a penny. We tried the
camera out on several micro-processing boards, eventually deciding to use the Pure Thermal board. Our
team also replaced the manual tripod with two small servo motors controlled by an Arduino board so that
it may pan and tilt the camera to capture the full room over a series of photos. The new IR camera and
motorized mount are shown in Figure 2. This serves as our current working prototype.

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Figure 2: The new miniature IR camera on its automated pan/tilt mount

While the physical device was streamlined, so too was the software process. What had been a
collection of individual commands was combined into one Python algorithm. This comprehensive code
points the camera in a direction, takes an image of each surface, identifies occupants, records the
temperatures, computes the comfort parameters, and then sends a heating or cooling signal wirelessly to
a thermostat. All of this is done with the click of a button. A visual representation of this full process is
shown in Figure 3.

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Figure 3: A flow chart of the full process from photo to HVAC signal

4.2 Enhancing Occupant detection
One issue with downsizing the camera is that the resolution is much lower than before. With a
larger camera, more pixels were captured with each image and it was easier for algorithms to detect an
occupant in the frame. A major hurdle in Phase 2 was finding a way to detect occupants in the frame
even at a lower resolution. We did this through Pythoon.
We use a number of open-source Python resources, known as libraries, to run the program. The
TensorFlow object detection library is what we use to identify occupants within the room. Other Python
packages we use include OpenCV, BAC0, serial, matplotlib, cvlib, pythermalcomfort, csv, and PIL. Refer
to each of its library documentation available online to understand the features of each package used in
the code.
At present, our occupant detection method works with 70 to 80% accuracy. The detection relies
on a box plot feature that exists in the cvlib library. We extract the box points within the 120 x 160 IR
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data frame to segment the human from the surrounding walls. At times, the points are not read or are
shown to as zero. Canny edge detection is an algorithm we added to enhance the detection accuracy.
The input IR grayscale image is also converted to an RGB image so that the TensorFlow object detection
algorithm can detect the person precisely. The results are shown below. The first picture in every group
shows the person detected with edge lines hidden and the second picture shows the edge being
detected based on the threshold set using the canny edge feature.

Figure 5: Occupant in a light room 16 feet away from the thermostat
Figure 4: Occupant in a dark room 14 feet from the thermostat
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Figure 6: Partial view of a seated occupant at 8 feet from the thermostat

We are satisfied with the current occupant detection as a functional starting point. Adding the
canny edge detection algorithm significantly improved performance over our previous approach. The
code is streamlined and some testing has been carried out in a private office setting. Over the month of
October, we were able to construct a program within Python to run experiments. The thermostat runs its
comfort analysis every 15 seconds and records the results along with its commands for heating and
cooling. The outputs are stored in an Excel file from which we can compare its commands to those of a
conventional thermostat. While we were not able to run the IR thermostat tests in as many environments
as we initially planned, we hope to continue testing and refining the code under the Murdock award
funding.
5. COMMERCIALIZATION
5.1 Market
One of the main goals of this phase of research was to bring the infrared thermostat technology
closer to commercialization. This infrared camera climate control system fits within the building controls
market, and more specifically, within the smart thermostat market. Frost & Sullivan predict that the smart
thermostat market is close to $1.6 billion globally and is expected to grow at a compound annual growth
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rate of 16.7% from 2018 to 2025 [6]. The research report estimates the value of integrating Artificial
Intelligence into building controls will generate a value of $6.08 billion in 2020. Market drivers include
utility programs that incentivize customers to adopt new thermostats to save energy and reduce peak
loads in the electricity sector. Market restraints for all smart thermostats include a lack of awareness of
the potential benefits, high initial costs, and exposure to cyber risks.
5.2 Competition
Market concentration in the smart thermostat sector is high, with the top five major companies --
Nest Labs (Google), Honeywell, Ecobee, Siemens, and Johnson Controls -- making up 57% of sales in
2018. The University of Idaho’s Office of Technology Transfer (OTT) team views these large companies
as potential investors for licensing rather than competitors. While some manufacturers and research
groups use infrared beams to measure surface temperatures, we have yet to find any manufacturer or
research group that combines surface temperatures, ventilation scaling, and glare management within a
single device. The manufacturer Diakin has an optional upgrade on their heat pumps that uses infrared
beams to adjust for surface temperatures and detect movement. However, IR beams cannot provide
occupancy counts in open spaces or use the standard ASHRAE comfort metrics. The Forrest Meggers
research group at Princeton also uses a device with infrared beams at the center of the room to
measure surface temperatures, but again, the beams do not count occupants or measure glare [7]. The
Meggers device only collects information on room geometry and surface temperatures.
The Center for the Built Environment at Berkeley has pursued surface temperature controls using
globe thermometers, thermocouples and Melexesis infrared sensors [8]. These devices are not capable
of counting occupants or measuring glare. Thermocouples require an electrical current to operate and
must be wired to each surface of interest, which is expensive and unsightly. A globe thermometer must
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be hung in an occupied area close to where the tenants are expected to work, which is similarly
inconvenient.
5.3 Future Funding and Development
The IDL team worked with the director of the Idaho Entrepreneurial Program at University of Idaho,
Dr. George Tanner. Dr. Tanner used the IR thermostat as a class project for some of his students to build
a business case for the product. While the students did some work, it was through collaboration with Dr.
Tanner that the team was well-positioned to apply for a Murdock Commercialization Initiation Program
Award. This award provides $31,000 of outside funding for product development, with the University of
Idaho committing a matching $31,000 towards this endeavor. With this funding, the research team will
spend the next year pursuing commercial investment. That pursuit includes partnering with the
University of Oregon to enhance the combined IP value of the system and protect it. The research team
at Oregon also has contacts with Lutron (a blinds control company) and the building controls branch at
Siemens. The research team will reach out to Avista on their industry contacts, whom they think would
be an appropriate funding partner.
Students from Idaho Entrepreneur Program will be funded on this project to develop a robust
business and marketing plan. They will begin by writing a report outlining the next steps for
commercialization. From this, they will develop targeted pitches for different investment avenues. Then,
with guidance from the program director, they will seek to secure investments either through venture
funding or licensing to a larger controls company. This is why some funding (13%) is set aside for
provisional patent filing. The research team has been working with the University’s Office of Technology
Transfer on this product for the last year and a half. The engineering faculty will continue to work with
OTT on the IP protection and marketing opportunities they suggest. The following table outlines the
specific outcomes and metrics for success from the Murdock Commercialization Award.
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Table 1: Goals for the Murdock Commercialization Phase
Outcomes Performance Measures
Robust glare detection algorithms
and blind controls successfully
integrated into the IR thermostat.

incorporates HDR images for glare detection.
2. Demonstration of blind control capabilities by sending
BACnet signals to an external device.
automated product prototype that
can be used in demonstrations
with potential investors

2. Laboratory validation of new prototype for comfort, glare,
and occupancy measurements in a relevant environment.
3. Computational algorithms and controls have been
combined into a streamlined package.

the University of Idaho, the University of Oregon, and
Avista Corporation.

marketing plan

2. Pitch development with prototype demonstration.
licensing partner

Avista Corporation and UO-ESBL.

6. DISCUSSION
The research work this year accelerated the IR thermostat concept from Technology Readiness
Level (TRL) 4 to TRL 6. We went from an initial proof of concept that relied on manual controls, to a
streamlined prototype that is fully automated. We secured funding from the University and the Murdock
Charitable Trust to move forward with commercialization. Jubin Mathai used this project as the basis for
his master’s thesis, which he successfully defended in May 2020. After graduation, Jubin accepted a job
running a cogeneration plant in New Hampshire largely because of his work on this project and the
research it afforded him on building controls and energy management systems. We look forward to
securing a provisional patent and pitching this technology to commercialization partners in 2021.
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7. BUDGET SUMMARY
These hours reflect only Avista’s contribution to this project and are not reflective of total project
investment by the research team, industry sponsors, or other university staff.
Personnel Hours estimate Description

FY19/FY21

Expense
PI/Faculty Salaries (Baker and Woods) $ 9,910
PI/Faculty Benefits (Baker and Woods) 3,063
Graduate Student Salaries 13,800
Student Benefits 435
Graduate Student Tuition Remission (partial) 9,352
Travel 0
Equipment 1,500
F&A / Overhead (Excludes Tuition) 14,440
Total $ 52,500

Indirect Costs
For this contract, UI-IDL was considered an on-campus unit of the University of Idaho with a federally
negotiated rate of 50.3%.

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8. REFERENCES
[1] E. Arens, M. Humphreys and H. Zhang, "Are 'Class A' temperature requirements realistic or
desirable?," Building and Environment, vol. 45, no. 1, pp. 4-10, 2010.
Clima 2007 WellBeing Indoors, Helsinki, 2007.
ings," Pacific Northwest National Lab Report 21596, Richland, WA, 2012.

Building and Environment, 2014.
https://ww2.frost.com/.
U.S. Patent Application No. 15/559,218
. Building and
175: 106779. DOI: 10.1016/j.buildenv.2020.106779.

APPENDIX E
Final Report: Gamification of Energy Use
Feedback
Gamification of Energy Use Feedback - Phase 1
A Final Report Submitted to Avista Corporation
for the Energy Research Initiative (AER) Program
For the University of Idaho:
Richard Reardon (Psychology and Organizational Sciences)
Julie Beeston (Computer Science)
Kellen Probert (Psychology, Human Factors)
Jode Keehr (Psychology, Human Factors)
David Beeston (Head of Information Systems, The Islands Trust)
October 9, 2020
Gamification-1
1
Major Sections
Executive Summary . . . . . 2
Introduction . . . . . . 3
Relevant Literature and History . . . 8
User Survey . . . . . . 10
Game Development . . . . . 15
Conclusions, Plans, and Potentials . . . 21
Budget Report . . . . . 22
References . . . . . . 24
APPENDIX A (Survey language and Appearence) . 26
APPENDIX B (Description of Data, Details of the Sample) 44
APPENDIX C (Analyses) . . . . 49
APPENDIX D (Additional Relevant Literature) . 90
Gamification-1
2
Executive Summary
We suggest that residential energy use can be reduced, or made more efficient, if
customers are motivated to pay attention to their current and past energy usage.
Awareness of performance, i.e., performance feedback, is essential to understanding the
relationship between actions and outcomes. Gamification, the use of the entertaining
aspects of games to produce behavior change, is proposed as a tool to encourage attention
to usage information. Thus far, the literature in the field with respect to such an
application to utility usage shows few attempts, with only partial, and unsustainable,
success. We know of no current gamification attempts at U.S. or Canadian utility
companies.

A survey was conducted with over 800 respondents in the Avista service area. The survey
was designed to assess game type preferences, incentive values, smart device and
computer usage, and some broad demographic variables (gender, income, age, etc.). The
survey results suggested that two types of games would be the best choices for
gamification: puzzle/word games and action games. The results also showed that the
major demographic variables did not differentially predict game type preference. This
permitted us to focus on a narrow range of game types, obviating the need to target game
types to particular demographic groups. The survey also revealed that personal savings
was the most important incentive for customers and potential customers and, importantly,
that desire for personal savings did not detract from other incentives, e.g., supporting
prosocial causes. Thus, the games selected for the gamification tool, though limited to
just two types, could have wide impact; and, play of those games could be incentivized
by a wide range of incentive types led by personal savings.
Several games were developed, and rapid prototyping undertaken. Based on that, two
games were selected for further development along with game-playing software. A
Chrome web browser running JavaScript code was chosen. The use of JavaScript
guaranteed that the games could be played on a wide array of devices and could be
resident on the Avista website (therefore they could interact securely with usage data).
The two games developed were a navigation/driving game and a sudoquote game. Simple
user testing was performed to refine the aesthetics of the games and to confirm
playability and entertainment value. A more detailed description of the games and links
to playable prototypes are included in this report.
The major obstacle yet to be overcome is to connect the games to actual customer usage
data. We have prototyped using random data but cannot test human user tendencies
without a linkage to real performance history. We presume that will be possible soon.
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Introduction
As major utility companies seek to reduce overall consumption, we are reminded
of the importance of understanding human user motivations, attention, decision-making,
and behaviors. Indeed, University of Dayton engineering professor Kevin Hallinan
(2014), suggests that behavioral changes alone could reduce consumption by a third.
Avista’s goal is to reduce, or optimize, overall energy consumption. This benefits
the customer in the form of savings on their energy bills; it benefits the utility because it
can then satisfy more customers with reliable, uninterrupted service. Energy use
reduction can be framed as a human performance problem (Boehm-Davis, Durso, & Lee,
2015). Typically, one imagines that improved performance is reflected in higher scores
on some behavioral measure. However, like golf and timed speed events, the
performance goal in energy conservation is to lower the score—lower scores indicate
better performance.
Feedback is a basic mechanism in most complex systems, certainly including
human performance systems. In human systems, feedback is essential to understanding
the relationship between effort, error, and optimal (or at least successful) performance.
The evidence is quite clear that if human users can be made explicitly aware of the
essential elements of their performance, they can modify that performance in the service
of improvement. However, this is only the case if they actually see the feedback, attend to
it, understand it, and have a readily available response (or responses). How might we
motivate humans to pay attention in cases where that attention is not immediately
compelled? Perhaps gamification can be helpful.
When we think of games in the context of computers and smart devices, we
typically start from the frame of reference of the play and the player. To be successful,
games must be engaging because play is voluntary. No one has to play a game. For
decades now, the electronic gaming industry has been effectively developing games that
are fun, captivating, and that sustain play. There is no single universally agreed-on
definition of “gamification” but, at its heart, it is the application of the entertaining and
engaging elements of games to human productivity and behavior change (Chou, 2015).
Game playing can be used to draw participants into situations in which they enjoy
processing educational materials, expose themselves to marketing messages, gain
information about competitors, and verify skills (to name just a few possibilities).
In this report, we will discuss the theoretical and practical issues tied to
gamification and energy conservation. We will report the results of literature searches
and implementation searches as well as our own survey on customer gaming behavior
and incentive preferences. We will outline the development process of two game
prototypes that we will propose as testing platforms for an application of gamification to
energy use feedback.
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“Little” Games and the Big Game
We have approached the conservation project with the view that there are really
two games, or game levels. The “Big Game” involves reduction in overall energy usage;
or, in some cases, making more strategic choices about usage that impact the utility and
fellow customers in beneficial ways (e.g., choosing the best time of day to use a
particular appliance). These days, conservation is an ethic that is widely accepted—the
starting point for most consumers is that conservation is good. For example, recent model
cars provide salient real-time information about whether driving behavior is better or
worse than mpg norms. Drivers report anecdotally that they find themselves using that
information, motivated by the underlying desire to conserve, to reduce gas consumption.
Their dashboard display tells them when they are in the reward zone. They are playing
the “Big Game” of automobile energy conservation.
Modern utility usage is not so much a moment-to-moment experience. It plays out
over days and months. Feedback about usage over previous months has been available on
monthly bills for many years. Monthly paper bills are almost a thing of the past; billing
and usage information are available through online accounts. It is possible to pay one’s
bill, or have it automatically paid, without ever seeing usage information; in the latter
case of automatic payment, a customer need never again access the utility site. Modern
smart meters can produce more timely and more frequent glimpses of usage data, but that
information is not salient—it has to be sought.
Our goal is to encourage customers to play the Big Game of energy conservation
by reducing or modifying their usage. This is fairly large-scale behavior change, and it
requires customers to monitor their power usage relative to their own past usage, or
relative to fellow customers. We believe that a set of smaller games could serve as
motivators to encourage customers to pay attention to their usage data. Such games could
then guide customers directly to conservation behaviors. They could also motivate other
behaviors that might indirectly lead to conservation. For example, the games could
encourage customers to run projections regarding future usage, provide recommendations
about energy efficient appliances, reveal tips about how to conserve, educate customers
about how utilities operate, and so on. The Avista website is rich with this kind of
information. However, at present, we suspect that customers visit the site only to pay
their bills or set up auto-pay. They do not linger.
We believe the smaller scale changes associated with small fun games can lead to
large scale change: The smaller games can push customers to attend to (and thus play) the
Big Game.
The reward structure in a gamification system as we propose will be complex.
Customers can be rewarded within the smaller game themselves with points that apply to
discounts, or that could be donated. Or, the earnings might be used within the games
themselves to affect future play. Information about how the Avista website operates will
determine how sophisticated we can be in managing rewards.
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Importantly, research in human performance and human attention suggests that
not everyone responds to the same motivational sticks and carrots (Drachen, Sifa,
Bauckhage, & Thurau, 2012; Heckhausen, & Heckhausen, 2005; Hilgard, Engelhardt, &
Bartholow, 2013; Yee, 2006). Our proposal begs an analysis of users’ individual
inclinations to use particular devices and software gaming applications, and the reasons
for those choices. This should be especially important to the utility as the best outcome
would be to have as many users as possible reducing their usage, not just a dedicated
subset that is attracted to a particular motivational system. The feedback provided can be
fairly uniform, but the motivations to attend to and follow that feedback could vary
(Carver & Scheir, 2001).
Thus, we felt it is important to survey likely Avista customers about the kinds of
incentives that were motivating to them, the kinds of games they preferred, the kind of
game playing behaviors they typically employed, and preferred, and likely game-playing
devices. The details of that survey can be found in Appendix A. The survey would help
guide game development and incentive choices.
Feedback systems work best if there is a readily available set of actions to the
person monitoring the feedback. For the Big Game, the actions available (e.g., thermostat
settings, efficient appliances, weatherizing) have to be made salient. The smaller games
can direct customers to those actions. The smaller games themselves should also have
available actions (banking/spending points, game settings, routing to other locations in
the website, playing again, etc.).

Assumptions

We began with some basic assumptions, and added to that set as we learned more.
(1) Smart meters will be routine, or a frequently chosen option. (2) The smart meter data
stream provides information on kilowatt-hour (kWh) usage, and that information can be
parsed down to five-minute intervals (but may not be available until the end of the day).
(3) Smart meter data can be used to illustrate past usage patterns, and has the potential to
be used in projections. (4) Useful usage information requires the establishment of a
baseline for each customer; thus, there will be delays from the time customers begin to
play game to the time when comparative usage can be assessed.
Research Issues
Here are the research issues we explored in the search for a gamification solution:
Are there any active applications of gamification systems in the utility industry at
present? What has been attempted, and what were the outcomes of those attempts?
The gaming literature suggests that there are differences in game style
preferences. Some games are tactical, some strategic. In some, play is team versus team
(e.g., neighborhood versus neighborhood, or alumni group versus alumni group), in
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others, play is individual versus individual, in still others, play is against the artificial
intelligence (AI) system.
Research and Development Plan Followed
Gamification-1
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rapid. The information we collected was used to make decisions about game types,
incentives, and customer profile possibilities.
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Relevant Literature and History
. Feedback systems have been tried. Many have failed, but there
have been some limited successes. The feedback systems that have been tried tend to fall
into one of two categories: First, social pressure (information about neighbors’ usage);
second, self-comparison (own usage over time; e.g., Geelen, et al, 2012). What is missing
is an analysis of users’ individual inclinations to use particular devices and software
applications, and for what reasons. This should be especially important to Avista, as the
best outcome would be to have as many users as possible reducing their usage, not just a
dedicated subset that is attracted to a particular motivational system. Similarly, the
feedback provided can be fairly uniform, but the motivations of customers to attend to
and follow that feedback are likely to vary.
Initiatives have been undertaken to use feedback systems to help customers
monitor their usage with emerging technologies. Unfortunately, many of these attempts
have been difficult to sustain. In some cases, promising “Apps” and software have been
abandoned as users have simply lost interest. For example, Google’s PowerMeter and
Microsoft’s Hohm were both discontinued prior to 2012 when they failed to generate
enough adopters. Ecova and Lucid partnered to enable its customers to reduce building
energy usage through Lucid’s “Building Dashboard” software. The software requires
some infrastructure, and its success seems to depend on the strong arm of company
policy to be successful. It is not, in itself, an attractive choice for individual consumers
(http://www.luciddesigngroup.com).
The gaming metaphor seems to work to some degree, with energy usage
decreasing for a period (http://adigaskell.org/2014/01/06/the-gamification-of-energy-
conservation/). Opower has been successful in providing feedback in the form of
comparisons to neighboring customers’ energy usage; the VP of Product Development,
Marc Laitin, stresses the importance of experimentation to refine the impact of behavioral
science. It seems that social pressure to conform to conservation efforts is one way to
achieve compliance (Cialdini, 2008, and http://www.opower.com/platform/behavioral-
science).
. There are over 3,200 utility providers in the
U.S. and Canada. Some of these are small regional customer-owned cooperatives (there
are several of these within or close to the Avista service footprint). Some are agencies of
local or regional governments, e.g., Seattle City Light. Then, there are many large and
mid-sized companies that are investor-owned, e.g., Avista, Pacific Gas and Electric
(PG&E) and Puget Sound Energy.
We conducted a search using appropriate keyword combinations (e.g., utility,
feedback, games, gamification) to discover if there were existing gamification systems in
place in these utilities. With respect to usage feedback, we found that the availability of
usage information was common if not universal. It has been common practice for
companies to provide usage information on monthly bills. Typically, this information has
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been in the form of kWh used per month, with perhaps a year’s worth of previous
months’ usage provided for comparison. This was often in the form of a small chart or
graph somewhere on the bill.
Some Interesting Starts. A Dutch company, Grendel Games
(https://grendelgames.com), has marketed themselves as a provider of gamification
services to various industries. Their most relevant game is called “Wijk & Water Battle”.
This game is sophisticated and graphically elaborate, and offers a metaphorical
simulation of water utility usage. The goal is to educate customers about how their
consumptive behaviors affect larger systems. The game is promising but does not interact
with customers’ actual usage.
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User Survey
To better understand where customers stand on relevant issues associated with the
project, a survey was planned and conducted. The survey was designed to tap into three
areas. First, basic demographic data was assessed with questions about age, gender,
income, home type and household composition, economic responsibilities for utilities,
and so on. The second section of the survey addressed customers’ sensitivity to various
kinds of incentives. The third section focused on customers’ game playing behavior and
game/device preferences. The survey ended with a set of funnel questions (each question
adding specificity) about their willingness to use the gaming system to manage their
utility usage if such management could produce outcomes that benefit them personally or
that help worthy causes. The survey data would allow us to draw conclusions about types
of games customers might prefer, the incentives that might motivate them, and the
relationship of games and incentives to variables that might be used in profile
construction, e.g., gender, income, age, etc.
Survey questions were submitted to the University’s Institutional Review Board
(IRB). The IRB ruled the survey “exempt” which permitted us to proceed with
administration.
The survey is included as APPENDIX A.
Qualtrics is a well-known company that has a license with the University to
provide survey tools. This project was beyond the scope covered with that license, so we
contracted with them for the additional effort. Respondents were recruited online. There
were two starting restrictions: Respondents had to be over 21 years of age, and they had
to reside within the Avista service area (Qualtrics ensures that by collecting ISP longitude
and latitude information for each respondent). A small monetary incentive was provided
to respondents. There is no way to guarantee that all respondents were Avista customers,
but if they were not, they were likely to be similar to Avista customers along key
dimensions (income distribution, experiences with weather, media exposure, etc.).
A total of 871 individuals responded to the online survey recruiting appeal. With
online survey data collection, it is not unusual to find that some respondents have failed
to take the survey seriously. Between survey tools and respondents’ own responses, these
respondents were identified, and their responses deleted before analyses began. The final
respondent total was 837. The rationale for elimination is provided in APPENDIX B.
APPENDIX B also provides basic descriptive data for key variables.
A more detailed exposition of report on our data analyses can be found in
APPENDIX C.
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Characteristics of the sample. Informal confidence about whether the sample
was “real” was bolstered by several measures.
Types of games. Before proceeding with game development, we assessed overall
preferences for game types. If only one or two types were preferred, then some economy
in game development would be gained. Also, we could look for any relationships
between game type preferences and potential profile characteristics (e.g., gender, age,
etc.). We asked our sample to evaluate the following game types:
Action games
Adventure games
Role-playing games
Simulation games
Strategy games
Sports games
Puzzle games
Word games
Game-playing preferences. We also asked about preferred game experiences,
exploring whether respondents preferred longer or shorter games, and whether they
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preferred to play against others or against the artificial intelligence within the game (i.e.,
solo). Respondents preferred shorter games to longer games and preferred to play against
the AI versus other opponents. The differences were not strong in either case, but they
helped us rule out Adventure games (which tend to be long and never-ending), Role-
playing games (which tend to be opponent-based), and Simulation games (which might
require additional equipment).
Incentives. We asked respondents to evaluate the importance of the following
incentives:
Personal savings on bill
Opportunity to donate to needy
Opportunity to donate to personal causes
Opportunity to donate to societal causes
Coupons for educational materials
Recreational downloads
Access to information, new, educational sites
Discounts toward energy saving purchases
User Profiles
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Nor will it be necessary to try to match customer characteristics with specific incentive
types. Everyone responds to personal savings, everyone wants to act prosocially, and
everyone wants to be entertained and to learn online. A small set of games can be
developed, and two classes of incentives (prosocial and instrumental) can be made
available for all customers.
Gender. Gender is the most obvious profile variable. The genders differ along
many dimensions of importance, so there was good reason to expect that gender might be
important in preferences for game types and incentives. The game-playing landscape has
changed in the last two decades; it is no longer dominated by males. Our data shows that
females enjoy playing as much as males (as is commonly reported in today’s gaming
literature). Males and females showed some differences in their game type preferences,
but these differences were not significant. Females rated all incentives as slightly more
important than did males, but did not differ from males on any particular incentive.
Females were more likely to indicate that they were more willing to play quick, fun
games if that play could accomplish any of the incentives offered. Both males and
females preferred shorter to longer games, with females indicating a slightly stronger
preference than males for shorter games. Both genders preferred solo play over play with
an opponent. Males and females were similar with respect to device usage, with smart
phone dominating.
Age. The age data was broken down into five categories by decade. All game type
preferences remained flat across age categories except for trends toward better rankings
for word and puzzle games as respondents were older. As the latter game types were the
most appealing and practical in general, profiling would have added nothing. Also, older
respondents were more likely to be less enthusiastic about educational and recreational
incentives but were otherwise similar to other age groups in how they evaluated the other
proposed game incentives. Older respondents were increasingly less likely to indicate that
they were willing to play quick, fun games if that play could accomplish any of the
incentives offered. Older groups preferred shorter to longer games; younger groups
showed no preference or only a slight preference for shorter games. All age categories
preferred solo play over play with an opponent, with older respondents particularly
preferring solo play. All age categories were similar with respect to computer and tablet
usage, with older groups showing a lessening of reliance on smart phones.
Income. Income data was broken down into five categories using a scheme that
was adapted from cutoffs used by the US Census Bureau and Pew Research Center. As
income tends to increase with age, we found some similarity between the income and the
age data. Higher income respondents tended to prefer word and puzzle games more than
adventure and action games compared to lower income respondents. Higher income
respondents also were less positive about incentives that involved education, recreation,
and online services, but were about as positive as other income levels with respect to
other incentive classes. Except for those in the top income category, respondents were
likely to indicate that they were equally willing to play quick, fun games if that play
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could accomplish any of the incentives offered. Those in the top incom category
expressed less interest. All income groups preferred shorter to longer games, with higher
income groups indicating a stronger for shorter games. All income groups preferred solo
play over play with an opponent, with the top income group indicating the strongest such
preference. All income groups were similar with respect to device usage, with smart
phone dominating.
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Game Development
Game development was tied directly to our survey results and user testing. The
survey helped us settle on two classes of games: word/puzzle games and action games.
This produced a broad range of games that were prototyped so they could be evaluated
and refined over three rounds of user testing. The basic elements required for the games
were the ability to access user energy usage data, the ability to understand the place of the
games in the Avista Energy Services Interface (ESI; elsewhere referred to simply as the
Avista website or site). Knowing the form of the data stream was critical to making final
decisions about the game programming platform. The games also had to be short. That is
consistent with preferences shown in our survey, and makes practical sense. Finally, of
course, the games had to be entertaining, with sustainable appeal.
It was also important to learn about the capabilities of the website. Once the
project was underway, we were able to get a briefing on the website’s capability as the
site was informed by new smart meters. Later in the project, we were able to get a
briefing on the form of the data stream and could modify our prototypes and
programming language choice accordingly.
We learned, and then could assume for development purposes, that “smart
meters” were being widely installed and would soon be a routine, or frequently chosen
option. We learned that the smart meter data stream provided information on kWh usage
that could be parsed down to five-minute intervals (which would not be available to users
or games until the end of the user day). We saw how smart meter data could be used to
illustrate past usage patterns over months and years.
The games must entertain Avista customers, tie directly to their usage data, point
them to best practices, and educate them about energy usage. We developed two games
from very different genres (a road navigation game and a Sudoku word game);
additionally, other games were prototyped and tested, as we note below. From a research
perspective, options were eliminated, and the reasons they were eliminated is probably
just as important as the reasons why other options were chosen to move forward.
. We started with several games in Round 1, all of
which fell within the action and puzzle/word game categories. All of these could be tied
to usage data, all could be played in a reasonably short time, and all could be handed over
to Avista fairly readily. The games were:
Helicopter madness (a targeting game)
Escape-cylinder metaphor (a 3-D maze game)
Word-sudoku (a combination word and number puzzle game)
Pattern matching (similar to Candy Crush)
Driving Game (road navigation)
Preparations for rapid prototyping were put in place for all of these games.
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Game development, Round 2. In Round 2, we began to narrow our focus.
Prototypes were created on multiple platforms. Simple user testing was conducted to
assess breadth of appeal, intuitive nature of the controls, users ability to connect their
play with energy usage and marketing possibilities. The following games “survived” that
testing:
Word-sudoku (a combination word and number puzzle game)
Road navigation (a driving game)
Both were easy to play, easy to explain, easy to connect to usage, and had
information presentation potential (for education and marketing) that could be subtly
embedded into the games. Two additional games of the original five, Helicopter Madness
and Escape Cylinder, were retained in prototype form for possible future development.

Game development, Round 3. In Round 3, a decision had to be made on a
programming platform. Both games could work well using multiple programming
platforms. Simple testing suggested that JavaScript was ideal. By this time (early Spring)
we had a better idea about the nature of the usage data stream, and JavaScript was well-
suited to interact with that stream. It also minimized security concerns because the game
could now reside within the Avista website and its security wall. Test users also found it
more comfortable to simply click on the game and begin play rather than download a
game App.
JavaScript also made it easier to write for both the iOS and Android operating
systems and associated devices. We found the typical problems associated with writing
for iOS, i.e., lack of developer access to programming that could take advantage of iOS
features and capabilities. In particular, we could not program haptic control into the
games. We did have that ability with Android OS devices, and hold out hope that haptic
control can be gained with iOS in the future. In the meantime, full function play is
possible because we have programmed keyboard, touchscreen, and mouse controls into
the games.
Round 2 continues as we periodically update and test aesthetics, graphics and
control features. The remaining two games match our initial objectives, and are written in
a manner that allows for future customization and enhancement. Details surrounding each
game follow.
The Road Navigation, or Driving Game
The driving game is the most entertaining from an action perspective and it is the
one most closely related to the user’s actual usage.
The primary goal of the game is to get around the track as quickly as possible and
the secondary goal is to collect as many stars as possible.
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Figure 1 shows the basic layout of the game. The top row of the heads up display
(HUD) shows the player’s current status in the game. From left to right it shows number
of stars collected, the current speed of the car, the fuel level of the car (that must be
refilled when empty) and the current number of seconds the user has spent.
The second line of the HUD is where the game is matched to the player’s energy
usage. The entire game runs on a twenty-four hour cycle with one second of game time
roughly corresponding to five minutes of usage time. In the figure, the usage time is
12:15 am, so the sky is dark and the foreground is dimmed. Things will brighten up when
the sun rises which gives the user a feel for morning, evening, day and night usage.
The user in the figure is using a lot of electricity for 12:15 am, but it is still below
a target threshold for usage, so there is a partially visible star on the usage bar. Any star
that is fully visible on the usage bar can be shaken loose (randomly) and falls to the road
below. In the image, you can see a star that previously fell to the road, and if the car
manages to hit that star, the star count will increase and the fuel will be instantly
replenished.
The bottom line is that the less electricity you use, the more stars there will be to
collect, and the less time you will have to spend refueling.
Figure 1: Screen shot of driving game.
The game also takes into account that there are some times during the day when it
is even more beneficial to conserve electricity because there is a high demand on the
system. During those times, the usage bar changes color and any stars collected during
those times are worth five regular stars.
This game also has some fun additions to make it more personal to the user. The
windmills on the right are actual Avista windmills, and many of the billboards show local
sites that the players might recognize.
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One final feature of this game is that even if the user is doing badly at energy
conservation and therefore runs out of fuel, the game gives the user energy saving tips
and quizzes while refueling.
Driving Game Benefits:
It seamlessly ties to usage data
Time of day data/peak usage times are incorporated
It is easily customizable to regions and seasons.
It has long-term maintainability
It is fun
A link to a playable prototype of the game is here (we are constantly refining the
games, so this is unlikely to be the most recent iteration of the game):
https://webpages.uidaho.edu/drbc/Avista/index.html
Sudoku Word Game
The sudoquote game combines the popular Sudoku game (where the player must
fill in each row, column and 3X3 sub-grid of a 9X9 grid with the digits from 1 to 9 with
no repeats), and cryptography puzzles. The two games are isomorphic so solving either
one solves both and the user can alternate between both systems to solve the puzzle. See
the Figure 2, below.
Figure 2: Screen shot of sudoquote game.
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The way the game works is that each cell of the 9X9 Sudoku grid contains a letter
from the quote, and solving the Sudoku defines the order that the letters appear in the
quote. Alternatively, solving a letter in the quote defines the number that belongs in the
square of the corresponding letter in the 9X9 grid.
The quote used in the game can be set by Avista, and can have an educational
theme (tips, information tidbits, nudges to other site pages, marketing theme, as the utility
chooses.

Sudoquote game benefits:
It is different enough from the driving game that it can pick up gamers who
are not interested in an action game.
It offers a more relaxed way to present energy saving tips, giving users more
time to consider them.
It is a novel combination of already popular puzzles.
It has long-term maintainability.
A link to a playable prototype of the game is here (we are constantly refining the
games, so this is unlikely to be the most recent iteration of the game):
https://webpages.uidaho.edu/drbc/Avista/index.html
The Dashboard
We have imagined a game interface that, for now, we are calling the gaming
“Dashboard”. The Dashboard would reside within the site security login. The Dashboard
would contain links to the games, but also links to other features of the Avista site.
Announcements could be available on the Dashboard as well as player information (game
success information, achievements earned, perhaps a leader board if group efforts are
possible).
The customer would access the Dashboard via one of three pathways. First, they
could access the site, as they normally have in the past, through the Avista home page
and the customer login link. Once inside, they would be presented with a link to the
gaming Dashboard along with the other links already there (as shown below). The second
pathway would be through a downloadable gaming Dashboard bookmark or
desktop/phone icon. Clicking on the bookmark or icon would take the customer to the
login window; successful login would put them directly on the Dashboard page. The third
pathway that could be considered would be to have downloadable bookmarks or icons for
the individual games. Clicking on one of them would take the customer to the login page;
successful login would then take the customer to the Dashboard at the precise location of
the link to that game. The location and appearance of that link is a decision for the Avista
web and marketing teams.
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The Dashboard has several important functions. It provides entry points for the
games. It highlights ready actions that game players can engage in before or after play. It
provides information about present and past play and it offers information about the
utility that might give customers a better understanding about current utility operations
(outages, shortages, external pressures, and so on).
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Conclusions, Plans, and Potentials
We have successfully accomplished much of what we planned and have learned
much along the way. We have confirmed that gamification techniques in the utility
industry are not in widespread use. We have some understanding of why earlier attempts
at feedback/gamification have been less successful.
We have learned much about the current capabilities of the Avista website, such
as the ability to offer customers five-minute interval next-day feedback on their usage.
We are familiar with the form of the feedback data stream. We have narrowed our game
types to two that have wide appeal. We have working prototypes of these games. With
that, and an understanding of the data stream, almost any game within those types is
possible.
We have ruled out the need to create customer demographic profiles. Regardless
of gender, age, or income, people seem to have similar tastes in games, and similar
desires with respect to what would be a desirable outcome (i.e., reward) for successful
game playing. This creates a helpful economy for further game development. It will not
be necessary to create a huge variety of games, each tailored for specific subsets of
customers.
There are a number of issues as we move into the second year of the project.
Some of these were unanticipated but revealed by our experiences during the first year.
We have to work out a better understanding of what would constitute “good”
conservation performance. It may not always be less energy consumption. Sometimes
what is helpful for the utility and fellow customers is choosing the right time of day to hit
household energy peaks. Working that into our measurements and reward system is
important. Also, all games invite abuse, so we must be mindful of the potential for game
players to “solve” the games and gain unfairly as a result. Finally, we have to always be
mindful of security. Locating the games within the Avista login solves many problems.
However, profound risk is involved as customer usage data can be mined by people of ill
intent.
Going forward, we hope to establish a direct connection with customer data
streams and explore the relationship of the games to other components of the Avista site.
If this direct connection cannot be established, we can compensate with simulation
procedure, but we will need to know soon. The games can be useful not only to have
customers attend to their own usage, but to explore emerging data capabilities such as the
ability to run projections. The games can also nudge customers to look for energy saving
tips on the site, to complete their on-site profile, to purchase energy efficient products, to
respond to prompts (e.g., daily safety and consumer messages), to process educational
modules (e.g., the Energy Plant Supervisor game), and to process Avista marketing and
branding information.
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Budget Report
The table on the next page shows the original budget request and actual
expenditures. The original budget request was for a total of $108,736. The amount
actually spent was $95,142.12, with $13,593.88 scheduled for return to Avista.
Funds in the major personnel categories were spent as planned for the efforts of
Richard Reardon and Julie Beeston (with small rounding discrepancies). Our survey
contractor, Qualtrics, performed acceptably well and was paid for their services. The
timing of David Beeston’s contributions had to be handled with his immigration status
carefully considered. David is a Canadian citizen and was, at certain times, unable to
formally accept a workload. Most of these issues were settled by the last quarter of the
project year, and his stipend was processed.
The major area of difference with our original budgetary plans centered on the
salary, tuition, fringe/health benefits of our graduate student Project Manager position. (It
was planned that a senior Human Factors doctoral student, Kellen Probert, would have
this position.) The timing of the award was important. The appointment of graduate
assistants is a delicate process because more than the student’s graduate stipend is in the
mix. That was especially true last year. A tragedy in the Human Factors Psychology
program in summer, 2019, occurred when a valued colleague, Brian Dyre, passed away.
Plans had to be developed to cover Brian’s teaching obligations. As Brian’s student,
Kellen was the best qualified doctoral student to step into one of the classes. Kellen’s
appointment was accompanied by tuition relief, as well as health coverage, through much
of the academic year. Graduate teaching assignments provide time for the student to
pursue research interests so Kellen was able to join the project, as we hoped, but he could
not accept the additional compensation (Graduate College rules do not permit
compensation levels beyond .5 even if the student is working more than that). Essentially,
in fall, 2019, Kellen worked on our project for free. Some of the funds not applied in fall
were used to compensate Kellen through summer, but the amount in the budget set aside
for tuition was not fully tapped because he had a partial tuition waiver (which also
covered his student health insurance).
We were not able to fund the undergraduate help we had planned to employ
because of the impending, and actual, Covid-19 shelter orders in late winter-early spring.
We had planned to travel to Spokane for in-person user testing in the later stages of the
project. This, of course, did not happen because of sheltering; travel funds were not spent.
The table shows requested and actual spending.
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Expense Request Actually
Spent
Initial Justification
PI/Faculty Salaries
(Richard Reardon,
PhD)
$18,810 $18,804.48 Avg. 4-5 hours/week fall-spring, 10 hours/week summer);
Total hours 286 per year.
PI/Faculty Fringe
(Richard Reardon,
PhD)
$5,812 $5,813.20 30.90% rate
Co-PI/Staff
Salaries (Julie
Beeston, Ph.D.)
$16,131.93$16,134 Avg. 4-5 hours/week fall-spring, 10 hours/week summer);
Total hours 286 per year
Co-PI/Staff Fringe
(Julie Beeston,
Ph.D.)
$4,986 $4,951.97 30.90% rate
Project Manager;
Graduate Assistant
in Human Factors
or Computer
Sciences
$12,127 $6,462 PhD student salary, 15 (semester 1) to 20 (semester 2)
hours/week per academic year at $20.73/hr; total of 585
hours/year
Manager Tuition $7,014 $5,889 Full-time student rate for 2 academic years prorated to level
of employment
Manager Heath
Insurance
$1,349 $00 Health insurance, $1799/year, prorated to level of
employment
Manager Fringe $412 $219.69 3.4% fringe rate
Undergraduate
Intern/Assistant
$00$975 Intern will spot-help with data collection, data management,
archival research, concept testing, and other duties as
needed; approx. 75 hours per year; pay rate: $13/hour
Undergrad.
Intern/Asst Fringe
$33 $00 3.4% rate
Technical
Consultant (David
Beeston)
$2,000 $2,000 25 hours each year; charged at the rate of $80/hour; no
benefits
Travel $493 $00 Mileage to/from Spokane/Moscow for presentations, updates
for funder, budget meetings, meetings with service provider,
user testing, etc. (approx. 1700 mi)
Qualtrics $5,000$5,000 Data collection services; will help gather test samples and
administer questionnaires and system trials
F&A/Overhead $33,591 $29,869.85 50.3% of direct costs above (Mgr. tuition & health insurance
excluded)
Project TOTAL $108,736 $95,142.12 (Difference: $13,593.88)
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References and Sources Used
Boehm-Davis, D., Durso, F., & Lee, J. (2015). APA Handbook of Human Systems
Integration. Washington, DC, American Psychological Association. (Multiple chapters in
this edited volume)
Charles, D. & Black, M. (2004). Dynamic Player Modelling: A Framework for Player-
centred Digital Games. International Conference on Computer Games: Artificial
Intelligence, Design and Education. Reading, WA: Microsoft Campus.
Chou, Y. (2015). Actionable Gamification: Beyond Points, Badges, and Leaderboards.
Octalysis Media.
Cialdini, R. (2008). Turning persuasion from an art into a science. In P. Muesburger, M.
Welker, & E. Wunder (Eds.), Clashes of Knowledge: Orthodoxies and Heterodoxies in
Science and Religion. Amsterdam: Springer.
Drachen, A., Sifa, R., Bauckhage, C., & Thurau, C. (2012). Guns, Swords and Data:
Clustering of Player Behavior in & Games in the Wild. Proceedings of the Annual
Meeting of the IEEE Conference on Computational Intelligence and Games. Grenada,
Spain.
Geelen, D., Keyson, D., Boess, S., & Brezet, H. (2012). Exploring the use of a game to
stimulate energy saving in households. Journal of Design Research, 10, 103-120.
Grossberg, F., Wolfson, M., Mazur-Stommen, S.,Farley, K., Nadel, S. (2015). Gamified
energy programs. Report Number B1501 prepared for The American Council for an
Energy-Efficient Economy, February, 2015.
Hallinan,K. (2014). http://adigaskell.org/2014/01/06/the-gamification-of-energy-
conservation/
Heckhausen, J. & Heckhausen, H. (2005). Motivation and Action. Cambridge, UK:
Cambridge University Press.
Hilgard, J., Engelhardt, C., & Bartholow, B. (2013). Individual differences in motives,
preferences, and pathology in video games: the gaming attitudes, motives, and
experiences scales. Frontiers in Psycholology, 9.
Yee, N. (2006). Motivations for play in online games. CyberPsychology & Behavior,
9(6), 772-775.
http://www.sense.com
http://www.opower.com/platform/behavi
or-science http://www.dropoly.com
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http://www.luciddesigngroup.com
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APPENDIX A
Survey Language and Appearance
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Avista1
Start of Block: Default Question Block
Q1 Hello: This survey is part of a project undertaken by the University of Idaho in
partnership with Avista Corporation. We are interested in how online games might make
it appealing to consumers to monitor their utility usage. Thank you for agreeing to answer
some questions for us.
University Institutional Review Boards (IRBs) evaluate research that involves human
respondents to ensure (1) that respondents are protected from unethical and risky
questions, and (2) that privacy is protected. This survey has been evaluated by the
University of Idaho's IRB. Your responses will be aggregated with those of many others;
any information that could identify you will be separated from the aggregated data.
The survey is in three parts. In the first part, we will ask you some questions about
yourself and about your utility usage and decision-making. In the second part, we will ask
you about the kinds of incentives that might make game playing particularly enjoyable to
you. Finally, we will ask you about some of your electronic game playing preferences
and interests.
The survey items appear on several screens. After completing a screen, click on the arrow
at the bottom right to get to the next screen.
End of Block: Default Question Block
Start of Block: Block 1
Q2 General Profile questions.
For each question below, use the scale provided to respond as truthfully as you can.
Q3 How old are you (please round to the nearest whole year)?
________________________________________________________________
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Q4 Do you identify as male or female?
o male (1)
o female (2)
Q5 Please indicate your household’s (all wage earners) approximate yearly income using
the scale below.
o less than $20,000/year (1)
o $20,000-30,000/year (2)
o $30,000-40,000/year (3)
o $40,000-50,000/year (4)
o $50,000-60,000/year (5)
o $60,000-70,000/year (6)
o $70,000-80,000/year (7)
o $80,000-90,000/year (8)
o $90,000-100,000/year (9)
o $100,000-110,000/year (10)
o $110,000-120,000/year (11)
o $120,000-130,000/year (12)
o $130,000-140,000/year (13)
o $140,000-150,000/year (14)
o over $150,000/year (15)
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Q6 If your household income is over $150,000 per year, please list that approximate
amount here.
________________________________________________________________
Page Break
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Q7 In your household, how much of the responsibility for payment and decision-making
regarding utilities, is yours. To respond, please drag the slider to the point on the scale
that represents the percentage of responsibility (from 0% to 100%) that is yours.
Note that if your response is zero, you must still manipulate the slider so that 0% appears
in a window above the slider (other wise, the system assumes you have not responded).
0 10 20 30 40 50 60 70 80 90 100
percent ()
Skip To: Q11 If In your household, how much of the responsibility for payment and decision-making
regarding utili... [ percent ] < 10
Q8 Which of the following statements represents your relationship with your utility
company or companies?
o one company supplies my electricity and my natural gas (2)
o one company supplies my electricity; a separate company supplies my natural gas (5)
o a company supplies my electricity; my residence is not capable of using natural gas (6)
Q9 Do you pay each month for that month’s usage, or do you pay an amount that
represents a running average of your bills?
o I pay for each month's usage (1)
o I have my bills averaged, and my monthly bill is computed from that average. (2)
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Q10 What kind of home do you live in?
o An apartment (which you may rent, or may own as a condominium) (1)
o An attached house (e.g., a townhouse) in which residences share common walls (which
you may own or rent). (2)
o A free-standing house (which you may rent or own)? (3)
o my utility expenses are included in my rent (1)
o I have an account with the utility company and pay them directly (2)
and consider it their primary
residence, i.e., live there over 50% of the time? (Your response would be 1 if you live
alone; 2 if you live with one other person, etc.)
________________________________________________________________
▢Yes; one of the people is my spouse or partner (5)
▢Yes; one of the people is an adult friend (6)
▢Yes; one of the people is an adult relative (a sibling, a parent, an adult child, etc.)
(8)
▢no; I am the sole adult (9)
▢I live alone and this question does not apply to me (10)
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Page Break
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Q13 Please respond with a simple "yes" or "no" to each of the following statements about
your household (whether house or apartment) energy usage.
yes (1)no (2)
I have central air conditioning
(1) o o
I have, and use, a wood (log or
pellet) stove or fireplace that
provides additional heat. (2) o o
I have an electric car, and a
home charging station for the
car. (3) o o
I run a small business from my
home, and do not have a
separate utility account for it.
(4) o o
I run expensive medical
equipment in my home (5) o o
I have installed solar panels
that provide household energy.
(6) o o
I have installed a windmill that
provides household energy. (7) o o
Household heating comes from
gas, not electricity. (8) o o
Q30 My biggest problem in trying to use less energy at home is _________ (please type
your answer in the box below; you can type as much as you want, but we would be happy
with 10-20 words).
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
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End of Block: Block 1
Q14 Incentives.
In this short section, we are interested in what kinds of incentives would be motivating to
you if they could be achieved by lowering your energy consumption. Simply indicate
how important each entry would be for you.
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Q15 How powerful or weak would each of these incentives be for you?
Very
powerful (1)
Moderately
powerful (2)
Neither
powerful nor
weak (3)
Moderately
weak (4)
Very weak
(5)
Personal
savings that
are applied to
my bill. (1) o o o o o
The
opportunity to
donate savings
to others in
need. (2)
o o o o o
The
opportunity to
donate savings
to worthy
causes that are
important to
me personally.
(3)
o o o o o
The
opportunity to
donate savings
to worthy
causes that are
important to
society. (4)
o o o o o
The
opportunity to
earn coupons
toward the
acquisition of
downloadable
educational
content (e.g.,
articles,
manuals,
books, and
videos). (5)
o o o o o
The
opportunity to
earn coupons
toward the
acquisition of
downloadable
recreational
o o o o o
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content
(games,
recreational
reading
materials,
etc.). (6)
The
opportunity to
earn coupons
toward
subscriptions
for online
services
(newsfeeds
and
newspapers,
movie
services,
professional
memberships,
etc.) (7)
o o o o o
The
opportunity to
earn discounts
toward energy
saving
products
(appliances,
bulbs,
thermostats,
etc.) and
services
(energy audits,
weather
proofing, etc.).
(8)
o o o o o
End of Block: Block 2
Start of Block: Block 3
Q16 Game playing.
In this section, we will ask you about some of your electronic game playing preferences
and practices.
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Q19 About how many hours per day, on average, would you say you play games on all of
your phone, tablet, computer, and other electronic devices (simply slide the indicator; the
precise number of hours and fractions of an hour you have chosen can be seen in a
window above the slider).
Note that if your response is zero, you must still manipulate the slider so that 0% appears
in the window above the slider (other wise, the system assumes you have not
responded).
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324
hours per day ()
Q20 Assign a percentage of time that you play games to each kind of device using the
sliders next to each device.
Note that if your response is zero on any of the 4 choices below, you must still
manipulate the slider so that 0% appears in a window above that slider (other wise, the
system assumes you have not responded).
percentage of time
0 10 20 30 40 50 60 70 80 90 100
personal computer ()
tablet ()
smart phone ()
other device ()
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Q21 Please indicate the extent to which each of these statements is true of you.
Definitely
true (18)
Probably true
(19)
Neither true
nor false (20)
Probably
false (21)
Definitely
false (22)
I enjoy
playing
electronic
games (1) o o o o o
I enjoy longer
games that I
can play over
hours and
days. (2)
o o o o o
I enjoy short
games that
offer me
challenges
that I can
solve in just a
few minutes.
(3)
o o o o o
I prefer games
that pit my
skills against
other players.
(4)
o o o o o
I prefer games
in which I am
pitted against
the computer
system, not
against other
players. (5)
o o o o o
I prefer games
that allow me
to challenge
my own
personal best.
(6)
o o o o o
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Q22 Please rank order, from most preferred to least preferred, your preferences for the
following types of games. Simply select and drag the entries into the order you prefer,
with the most preferred toward the top.
______ action games (shooting, escaping, chasing, tactical combat, etc.) (1)
______ adventure games (games involving journeys, collecting of weapons or artifacts, reaching
various levels of accomplishment, often with heroic storylines, etc.) (2)
______ role-playing games (similalr to adventure games, but with defined characters that players
represent, fantasy settings that may involve different eras and customs, etc.) (3)
______ simulation games (games that simulate real world tasks and adventures, e.g., flight and
other vehicle simulators, community simulators like SimCity, etc.) (4)
______ strategy games (games that emphasize strategic planning for a large number of
interconnected variables, e.g., warcraft games) (5)
______ sports games (racing, baseball, football, golf, etc.) (6)
______ puzzle games (mazes, blocks, lines, etc., where a solution is required before another game
is offered; card games would also apply here, as would games like Tetris, mahjong, etc.) (7)
______ word games (games that challenge spelling, meaning, vocabulary generation, etc.;
examples might be Boggle, crossword puzzles, etc.) (8)
Page Break
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Q29 My favorite games are ones that ___________ (please type your response in the box
below; you can type as much as you want, but we would be happy with 10-15 words).
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
Q31 Currently, my favorite game is _________ .
________________________________________________________________
Page Break
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Q24 The next three questions are more complex than they seem; each question adds a
little bit more to the previous one ("ups the ante", so to speak). So, read carefully.

I would be interested in quick, challenging games that can be played at idle moments
(waiting for transportation, waiting for an appointment, killing a few minutes between
meetings, events, shows, etc.).
o Strongly agree (1)
o Somewhat agree (2)
o Neither agree nor disagree (3)
o Somewhat disagree (4)
o Strongly disagree (5)
Q25 I would be interested in playing quick games if my play could be tied to my energy
usage, and I could conserve energy with successful play.
o Strongly agree (1)
o Somewhat agree (2)
o Neither agree nor disagree (3)
o Somewhat disagree (4)
o Strongly disagree (5)
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Q26 I would be interested in playing quick games if my play could be tied to my energy
usage, I could conserve energy with successful play, and my savings could be applied to
my bill, or used to support worthy causes.
o Strongly agree (1)
o Somewhat agree (2)
o Neither agree nor disagree (3)
o Somewhat disagree (4)
o Strongly disagree (5)
End of Block: Block 3
Start of Block: Block 4
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Q28 Occasionally, we find it very helpful to have a brief follow-up phone chat with
survey respondents. We hope you agree that helping utility users save energy and money
is a worthy cause.

If we may contact you for such a follow-up, please put your name and phone number
below.

Please click on the arrow below to end and submit the survey. Thanks again.

o Name (4) ________________________________________________
o Phone # (5) ________________________________________________
End of Block: Block 4
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APPENDIX B
Description of Data, Details of the Sample
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Summary Survey Outcomes (May 1 set)
Some Characteristics of the Sample
Qualtrics records the longitude and latitude of respondents ISP node. Our glance
indicates that respondents were overwhelmingly within the Avista Service area. There
may be a handful of exceptions (perhaps travelers); so few that it probably makes sense
to keep them in the sample.
The numbers reported may differ in totals because of occasional blank responses.
All respondents were fluent in English (though we cannot guarantee that English was
always their first language).
The sample originally included 871 respondents. However, we reduced that number to
837 by deleting 34 respondents who clearly did not take the survey seriously (this is
always a risk with such survey methods). Eliminations occurred for various reasons.
Some of those eliminated gave nonsensical answers (e.g., indicating a household size of
100, some typed random letters and numbers into open response fields, and so on). Some
were eliminated because of response times. Average time to complete the survey was
17.65 minutes, a time that well-exceeded our estimate of about 11 minutes (calculated
with university student test subjects in survey development). However, this was skewed
by a handful of respondents who took well over an hour (e.g., one person took 16 hours,
another 49 hours). Also, we were concerned that some respondents may have taken too
little time to be thoughtful (several took took fewer than 200 seconds to complete the
survey). Our decision was to eliminate any respondents who took longer than an hour.
Then, we set the lower cutoff by evaluating the coherence of the open-ended responses.
Below 4 minutes, open-ended responses became noticeably less coherent. By eliminating
respondents who took less than 4 minutes, or more than one hour, we reduced the mean
response time to 11.47 minutes (688 sec), which was in line with our pre-survey
estimates. With all elimination decisions, anything less than complete agreement by the
investigators resulted in a respondent remaining in the final sample.
The remaining 837 respondents included 655 females and 182 males. Women typically
respond at higher rates, so this was not particularly worrisome.
The average age of respondents was 46.17 years. (The average ages of the male and
female subgroups were 48.90 years and 45.41 years, respectively.)
Respondents indicated their income level by placing themselves within $10,000 groups
(i.e., “below $20,000”, “$20,000-30,000”, “$30,000-40,000”, etc. up to “over
$150,000”). Income was skewed to the lower end with the mode of the distribution
falling in the $30,000-40,000 range and the median falling in the $40,000-50,000 range.
Our figures are consistent with anecdotal reports from Avista about income levels for
their customers. Importantly, they are consistent with national statistics that show that the
largest frequencies of families fall within the $15,000-40,000, range, with the mode in the
$30,000-35,000 sub-category. (The median data for the US was $63,179 in 2018; the
Gamification-1
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higher figure here, compared to our sample, reflects the Census Bureau’s different
method of handling higher income levels).
The sources most often used to categorize income levels are those from the U.S. Census
Bureau and from Pew Research Center. These systems differ from each other in small
ways, so we employ a system that is consistent with these sources, but does not exactly
match either one. The principle difference is that we include the category of Low Middle
Income. (Pew and The Census Bureau from low income to middle income to upper
middle. We chose $70,000 as the splitting point because it was approximately halfway
between the low income and middle income cutoff points.)
o Near or below the poverty line: less than $19,999 (187/22%; Ma: 33/4%; Fe:
154/18%)
o Low income: $20,000-39,999 (211/25%; Ma: 48/6%; Fe: 163/19%)
o Low Middle Income: $40,000-69,999 (210/25%; Ma: 53/6%; Fe: 157/19%)
o Middle Income: $70,000-139,999 (197/24%; Ma: 40/5%; Fe: 157/19%)
o Upper middle income (and higher): 140,000 and above (32/4%; Ma: 8/1%; Fe:
24/3%)
The number of respondents in these categories/percent of total (837), as a total and by
gender, is provided in parentheses following the category description.
Utility Usage
The average respondent’s percentage of input into utility decision-making was 69.66%
(Ma: 69.46; Fe: 69.73). We can eliminate respondents who have no responsibility later if
needed. A key figure is that only 36 respondents (4%) reported having no responsibility
for utility decision-making.
386 (46%) respondents lived in all-electric households; 166 (20%) lived in households
where electricity and gas were supplied by the same utility; 228 (27%) lived in
households where gas and electricity were supplied by different companies. There were
57 (7%) blank responses (typically, customers who had no responsibility for their bill, or
who had their utilities included in their rent).
623 (74%) respondents pay their bill by monthly usage rather than bill averaging; 157
(19%) respondents average. (780 responses; 57 (7%) left this blank typically because
they had no responsibility for their bill, or had their utilities included in their rent.)
499 (60%) of respondents live in free-standing houses; 203 (24%) live in apartments; 78
(9%) lived in attached homes/townhouses/duplexes, etc. (again, there were 57 (9%) blank
responses).
680 (81%) respondents reported that they are billed by their utility company (or
companies) for usage; 157 (19%) indicated that their utility expenses were included in
their rental charges.
The average household in our sample had 2.81 residents, with much variability. 88 (11%)
respondents lived alone; 57 (7%) were sole adults in a household with others; 167 (20%)
were adults who lived with an adult friend or relative (not their partner or spouse); 468
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(56%) reported living with a spouse or partner (and perhaps children; through a
combination of survey items, we can separate parents who have children and those who
do not).

Other capabilities or drains. Respondents who had each of the following conservation
capabilities, or drains, are indicated.
o Central air (drain) – 321 (38%)
o Wood stove/wood heat source (capability) – 222 (27%)
o Electric car (drain) – 29 (3%)
o Ran a business from home (drain) – 82 (10%)
o Medical equipment (drain) – 40 (5%)
o Solar capability (capability) – 32 (4%)
o Wind capability (capability) – 15 (2%)
o Gas heat (capability) - 263 (31%)
Incentives – 1 (By convention, “M” is used to refer to the statistical mean.)
How important are these incentives to you? Responses 1-5, where:
Very weak (1); Moderately weak (2); Neither powerful nor weak (3);
Moderately powerful (4); Very powerful (5)
o Personal savings on bill; M = 4.05
o Opportunity to donate to needy; M = 3.16
o Opportunity to donate to personal causes; M = 3.19
o Opportunity to donate to societal causes; M = 3.08
o Coupons for educational materials; M = 2.86
o Recreational downloads; M = 2.95
o Access to information, new, educational sites; M = 2.85
o Discounts toward energy saving purchases; M = 3.65
Game Playing (By convention, “M” is used to refer to the statistical mean.)
Hours on computers or other devices playing games? M = 4.08 hours/day
What devices? (these were respondents’ estimates by category, thus percentages do not
need to add to 100)
o Personal computer: M = 16%
o Tablet: M = 14%
o Smart phone: M = 46%
o Other: M = 10%
Electronic games
Definitely false (1)-Probably false (2)-Neither true nor false (3)-Probably
true (4)-Definitely true (5)
o I enjoy in general: M = 3.97
o I enjoy longer games: M = 3.26
o I enjoy short games: M = 3.66
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o I want to play against others: M = 2.82
o I want to play against the system: M = 3.14
o I prefer to play toward my personal best: M = 3.87
Preferred game types (ranked; lower numbers mean higher ranking)
o Action games: M = 4.38
o Adventure games: M = 3.61
o Role-playing games: M = 4.27
o Simulation games: M = 4.43
o Strategy games: M = 4.70
o Sports games: M = 6.45
o Puzzle games: M = 3.72
o Word games: M = 4.43
Incentives – 2 (By convention, “M” is used to refer to the statistical mean.)
I would be interested in quick, challenging games that can be played at idle moments
(waiting for transportation, waiting for an appointment, killing a few minutes between
meetings, events, shows, etc.) M = 3.99
where: Strongly disagree (1); Somewhat disagree (2); Neither agree nor
disagree (3); Somewhat agree (4); Strongly agree (5)
I would be interested in playing quick games if my play could be tied to my energy
usage, and I could conserve energy with successful play. M = 3.59
where: Strongly disagree (1); Somewhat disagree (2); Neither agree nor
disagree (3); Somewhat agree (4); Strongly agree (5)
I would be interested in playing quick games if my play could be tied to my energy
usage, I could conserve energy with successful play, and my savings could be applied to
my bill, or used to support worthy causes. M = 3.77
where: Strongly disagree (1); Somewhat disagree (2); Neither agree nor
disagree (3); Somewhat agree (4); Strongly agree (5)
Gamification-1
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APPENDIX C
Analyses
Notes
Statistical Analyses were performed with the JASP Statistical Package (supported
by the University of Amsterdam).
The Y-axis of graphs was auto-adjusted to fit the graph space. Differences are
sometimes larger, but more often smaller, than they appear.
The large sample size guaranteed that the analyses would be over-powered. Thus,
the size-of-effect values (in this case, we used 2p, i.e., partial eta-squared) were as
important as the probability values (p). Small 2p values can mean that an effect,
though significant, is so small that it is trivial, and thus impractical.
The analyses reported here serve the main points of the project (understanding
relevant customer behaviors and assessing profile possibilities) but we continue to
analyze the data for useful, serendipitous findings.
Gamification-1
50
Principal Component Analyses
By Game Types
By Incentive Types
Gamification-1
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Principal Component Analysis – Game Types
Chi-squared Test
Value df p
Model 4538.419 7 < .001

Component Loadings
RC1 RC2 RC3 Uniqueness
Action 0.711 0.358
Advntr 0.438 0.437
RolePlay -0.522 0.439
Simul 0.574 0.495
Strategy 0.790 0.374
Sports 0.855 0.332
Puzzle -0.928 0.190
Word -0.931 0.178
Note. Applied rotation method is promax.

Component Characteristics
Eigenvalue Proportion var. Cumulative
RC1 2.754 0.344 0.344
RC2 1.241 0.155 0.499
RC3 1.201 0.150 0.650

Component Correlations
RC1 RC2 RC3
RC1 1.000 -0.303 -0.158
RC2 -0.303 1.000 -0.024
RC3 -0.158 -0.024 1.000

Gamification-1
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Path Diagram
Scree plot
Gamification-1
53
Principal Component Analysis – Incentive Types
Chi-squared Test
Value df p
Model 287.435 13 < .001

Component Loadings
RC1 RC2 Uniqueness
PersSvngs 0.517 0.728
Needy 0.908 0.217
PersCaus 0.926 0.168
SocCaus 0.867 0.202
EdContent 0.827 0.270
RecrCntnt 0.900 0.218
OnLnServ 0.894 0.269
EnrgyProd 0.654 0.487
Note. Applied rotation method is promax.

Component Characteristics
Eigenvalue Proportion var. Cumulative
RC1 4.145 0.518 0.518
RC2 1.295 0.162 0.680

Component Correlations
RC1 RC2
RC1 1.000 0.527
RC2 0.527 1.000

Gamification-1
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Path Diagram
Gamification-1
55
Gender Effects
Acronyms for Game Types
Action Games = act
Adventure Games = adv
Role Play Games = rlpl
Simulation Games/Simulators = siml
Strategy Games = strt
Sports Games = sprts
Puzzle Games = pzzl
Word Games = word
Gender Categories
1 = male
2 = female
Acronyms for Incentive Types
Personal Savings = svngs
Help the Needy = needy
Personal Causes = pcaus
Social Causes = scaus
Educational Content = edcnt
Recreational Content = recr
Online Services = olsrv
Energy Products = prods
Gamification-1
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M-F  Game Types
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 2723.632 ᵃ 7 ᵃ 389.090 ᵃ 77.564 ᵃ < .001 ᵃ 0.085
RM Factor 1 ✻ M-F 1323.418 ᵃ 7 ᵃ 189.060 ᵃ 37.688 ᵃ < .001 ᵃ 0.043
Residuals 29320.837 5845 5.016
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
M-F 0.009 1 0.009 0.972 0.324 0.001
Residuals 7.591 835 0.009
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 M-F Mean SD N
act 1 2.901 2.139 182
2 4.795 2.425 655
adv 1 3.484 1.736 182
2 3.640 1.837 655
pzzl 1 4.742 2.382 182
2 3.431 2.321 655
rlpl 1 4.060 2.076 182
2 4.325 1.988 655
siml 1 4.451 1.899 182
2 4.418 1.929 655
sprts 1 5.824 2.022 182
2 6.626 1.611 655
strt 1 4.665 1.861 182
2 4.715 1.945 655
word 1 5.841 2.450 182
2 4.040 2.557 655

Gamification-1
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Descriptives plots

Post Hoc Tests
holm
1 2 -0.003 0.003 -0.986 0.324
Note. Results are averaged over the levels of: RM Factor 1
Gamification-1
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M – F “Enjoy Playing”
ANCOVA - EnjPlay
Cases Sum of Squares df Mean Square F p
M-F 1.729 1 1.729 1.181 0.277
Residuals 1222.266 835 1.464
Note. Type III Sum of Squares

Descriptives
Descriptives - EnjPlay
M-F Mean SD N
1 3.879 1.299 182
2 3.989 1.184 655

Descriptives plots
Gamification-1
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M - F “Prefer playing longer games” / “Prefer playing shorter games”
Within Subjects Effects
Cases Sum of Squares df Mean Square F p
RM Factor 1 28.527 1 28.527 20.268 < .001
RM Factor 1 ✻ M-F 6.083 1 6.083 4.322 0.038
Residuals 1175.277 835 1.408
Note. Type III Sum of Squares

Between Subjects Effects
Cases Sum of Squares df Mean Square F p
M-F 10.401 1 10.401 4.837 0.028
Residuals 1795.417 835 2.150
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 M-F Mean SD N
long 1 3.225 1.467 182
2 3.270 1.442 655
short 1 3.396 1.312 182
2 3.733 1.180 655

Descriptives plots
Gamification-1
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M - F  “Prefer playing solo” / “Prefer playing opponents”
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 32.770 1 32.770 27.333 < .001 0.032
RM Factor 1 ✻ M-F 0.454 1 0.454 0.379 0.538 4.537e -4
Residuals 1001.095 835 1.199
Note. Type III Sum of Squares

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
M-F 7.401 1 7.401 3.397 0.066 0.004
Residuals 1819.095 835 2.179
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 M-F Mean SD N
opp 1 2.912 1.392 182
2 2.791 1.329 655
solo 1 3.291 1.269 182
2 3.090 1.251 655
Descriptives plots
M – F  Incentive Types
Gamification-1
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Acronyms
Personal Savings = svngs
Help the Needy = needy
Personal Causes = pcaus
Social Causes = scaus
Educational Content = edcnt
Recreational Content = recr
Online Services = olsrv
Energy Products = prods
η² p
RM Factor 1 650.924 ᵃ 7 ᵃ 92.989 ᵃ 117.620 ᵃ < .001 ᵃ 0.123
RM Factor 1 ✻ M-F 13.012 ᵃ 7 ᵃ 1.859 ᵃ 2.351 ᵃ 0.021 ᵃ 0.003
Residuals 4620.981 5845 0.791
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

η² p
M-F 12.336 1 12.336 2.125 0.145 0.003
Residuals 4846.016 835 5.804
Note. Type III Sum of Squares

Descriptives
edcnt 1 2.824 1.267 182
2 2.870 1.297 655
needy 1 2.984 1.177 182
2 3.153 1.138 655
olsrv 1 2.923 1.276 182
2 2.826 1.293 655
pcaus 1 3.011 1.208 182
2 3.246 1.104 655
prods 1 3.610 1.242 182
2 3.663 1.152 655
recr 1 2.912 1.271 182
2 2.960 1.270 655
scaus 1 2.978 1.217 182
Gamification-1
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Descriptives
RM Factor 1 M-F Mean SD N
2 3.113 1.125 655
svngs 1 3.857 1.143 182
2 4.101 1.036 655

Descriptives plots

Assumption Checks
Test of Sphericity
Mauchly's
W
Approx.
Χ² dfSphericity p-
value
Greenhouse-
Geisser ε
Huynh-
Feldt ε
Lower
Bound ε
RM
Factor
1
0.239 1191.909 27 < .001 0.690 0.695 0.143

Post Hoc Tests
Post Hoc Comparisons - M-F
Mean Difference SE t p holm
1 2 -0.104 0.071 -1.458 0.145
Note. Results are averaged over the levels of: RM Factor 1
Gamification-1
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M - F  Quick 1-2-3
(Quick 3: “I would be interested in playing quick games if my play could be tied to my
energy usage, I could conserve energy with successful play, and my savings could be
applied to my bill, or used to support worthy causes.”)
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 44.725 ᵃ 2 ᵃ 22.363 ᵃ 41.257 ᵃ < .001 ᵃ 0.047
RM Factor 1 ✻ M-F 0.443 ᵃ 2 ᵃ 0.222 ᵃ 0.409 ᵃ 0.664 ᵃ 4.895e -4
Residuals 905.198 1670 0.542
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
M-F 60.940 1 60.940 20.095 < .001 0.024
Residuals 2532.167 835 3.033
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 M-F Mean SD N
quick 1 1 3.709 1.247 182
2 4.067 1.137 655
quick 2 1 3.319 1.193 182
2 3.670 1.141 655
quick 3 1 3.440 1.331 182
2 3.863 1.161 655

Gamification-1
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Descriptives plots
Gamification-1
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Age Effects
Acronyms for Game Types
Action Games = act
Adventure Games = adv
Role Play Games = rlpl
Simulation Games/Simulators = siml
Strategy Games = strt
Sports Games = sprts
Puzzle Games = pzzl
Word Games = word
Age Categories
1 = 20-29
2 = 30-39
3 = 40-49
4 = 50-59
5 = 60-69
6 = 70+
Acronyms for Incentive Types
Personal Savings = svngs
Help the Needy = needy
Personal Causes = pcaus
Social Causes = scaus
Educational Content = edcnt
Recreational Content = recr
Online Services = olsrv
Energy Products = prods
Gamification-1
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Age Category  Game Types
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 4167.336 ᵃ 7 ᵃ 595.334 ᵃ 118.251 ᵃ < .001 ᵃ 0.125
RM Factor 1 ✻ AgeCat 1358.740 ᵃ 35 ᵃ 38.821 ᵃ 7.711 ᵃ < .001 ᵃ 0.044
Residuals 29285.514 5817 5.034
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
AgeCat 0.025 5 0.005 0.553 0.736 0.003
Residuals 7.575 831 0.009
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 AgeCat Mean SD N
act 1 4.197 2.520 147
2 4.183 2.444 180
3 4.406 2.466 160
4 4.415 2.561 142
5 4.528 2.428 142
6 4.909 2.582 66
adv 1 3.170 1.780 147
2 3.339 1.840 180
3 3.725 1.832 160
4 3.761 1.746 142
5 3.937 1.767 142
6 3.970 1.806 66
pzzl 1 3.442 2.044 147
2 4.022 2.095 180
3 4.412 2.035 160
4 4.592 1.917 142
5 4.761 1.782 142
6 4.667 1.667 66
rlpl 1 3.741 1.952 147
2 4.378 1.906 180
Gamification-1
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Descriptives
RM Factor 1 AgeCat Mean SD N
3 4.438 1.938 160
4 4.465 1.912 142
5 4.965 1.850 142
6 4.803 1.591 66
siml 1 4.599 1.678 147
2 4.256 1.989 180
3 4.669 1.916 160
4 4.880 1.930 142
5 4.901 2.060 142
6 5.439 1.711 66
sprts 1 6.667 1.697 147
2 6.428 1.743 180
3 6.463 1.744 160
4 6.296 1.786 142
5 6.387 1.786 142
6 6.485 1.620 66
strt 1 4.816 2.193 147
2 4.128 2.335 180
3 3.600 2.455 160
4 3.493 2.452 142
5 2.937 2.125 142
6 2.576 2.091 66
word 1 5.347 2.247 147
2 5.244 2.478 180
3 4.287 2.607 160
4 4.056 2.686 142
5 3.585 2.658 142
6 3.152 2.531 66

Descriptives plots
Gamification-1
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Gamification-1
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Age Category – “Enjoy playing games”
ANCOVA - EnjPlay
Cases Sum of Squares df Mean Square F p η² p
AgeCat 49.769 5 9.954 7.044 < .001 0.041
Residuals 1174.226 831 1.413
Note. Type III Sum of Squares

Descriptives
Descriptives - EnjPlay
AgeCat Mean SD N
1 4.245 0.911 147
2 4.206 1.050 180
3 3.900 1.235 160
4 3.930 1.207 142
5 3.732 1.357 142
6 3.424 1.510 66

Descriptives plots
Gamification-1
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Age Category  “Prefer playing longer games” / “Prefer playing shorter
games”
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 67.618 1 67.618 49.716 < .001 0.056
RM Factor 1 ✻ AgeCat 51.120 5 10.224 7.517 < .001 0.043
Residuals 1130.240 831 1.360
Note. Type III Sum of Squares

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
AgeCat 170.670 5 34.134 17.347 < .001 0.095
Residuals 1635.148 831 1.968
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 AgeCat Mean SD N
long 1 3.830 1.252 147
2 3.689 1.300 180
3 3.294 1.417 160
4 3.141 1.422 142
5 2.542 1.442 142
6 2.545 1.416 66
short 1 3.789 1.112 147
2 3.822 1.178 180
3 3.688 1.177 160
4 3.641 1.163 142
5 3.585 1.284 142
6 3.061 1.435 66
Descriptives plots
Gamification-1
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Gamification-1
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Age Category  “Prefer playing solo” / “Prefer playing opponents”
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 58.767 1 58.767 51.430 < .001 0.058
RM Factor 1 ✻ AgeCat 52.006 5 10.401 9.103 < .001 0.052
Residuals 949.544 831 1.143
Note. Type III Sum of Squares

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
AgeCat 126.221 5 25.244 12.338 < .001 0.069
Residuals 1700.275 831 2.046
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 AgeCat Mean SD N
opp 1 3.361 1.287 147
2 3.161 1.278 180
3 2.900 1.294 160
4 2.669 1.242 142
5 2.338 1.315 142
6 1.818 1.094 66
solo 1 3.218 1.168 147
2 3.228 1.186 180
3 3.100 1.204 160
4 3.204 1.176 142
5 3.035 1.396 142
6 2.833 1.565 66

Descriptives plots
Gamification-1
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Age Category  Incentive Types
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 972.483 ᵃ 7 ᵃ 138.926 ᵃ 177.482 ᵃ < .001 ᵃ 0.176
RM Factor 1 ✻ AgeCat 80.664 ᵃ 35 ᵃ 2.305 ᵃ 2.944 ᵃ < .001 ᵃ 0.017
Residuals 4553.328 5817 0.783
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
AgeCat 191.513 5 38.303 6.820 < .001 0.039
Residuals 4666.838 831 5.616
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 AgeCat Mean SD N
edcnt 1 3.122 1.287 147
2 3.167 1.288 180
3 2.962 1.345 160
4 2.641 1.228 142
5 2.479 1.213 142
6 2.485 1.099 66
Gamification-1
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Descriptives
RM Factor 1 AgeCat Mean SD N
needy 1 3.245 1.138 147
2 3.283 1.100 180
3 3.019 1.200 160
4 3.092 1.166 142
5 2.908 1.142 142
6 3.106 1.083 66
olsrv 1 3.088 1.276 147
2 3.083 1.303 180
3 2.962 1.288 160
4 2.697 1.238 142
5 2.521 1.281 142
6 2.409 1.150 66
pcaus 1 3.401 1.083 147
2 3.278 1.031 180
3 3.212 1.210 160
4 3.077 1.099 142
5 2.930 1.207 142
6 3.288 1.106 66
prods 1 3.571 1.233 147
2 3.772 1.118 180
3 3.850 1.047 160
4 3.549 1.188 142
5 3.500 1.276 142
6 3.561 1.139 66
recr 1 3.286 1.244 147
2 3.156 1.263 180
3 3.144 1.283 160
4 2.725 1.233 142
5 2.556 1.252 142
6 2.500 1.011 66
scaus 1 3.293 1.148 147
2 3.211 1.067 180
3 3.044 1.220 160
4 3.007 1.069 142
5 2.782 1.174 142
6 3.182 1.149 66
svngs 1 4.075 0.966 147
2 4.150 0.960 180
3 4.112 0.932 160
4 4.021 1.151 142
Gamification-1
75
Descriptives
RM Factor 1 AgeCat Mean SD N
5 3.958 1.220 142
6 3.803 1.255 66

Descriptives plots
Gamification-1
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Age Category  Quick 1-2-3
(Quick 3: “I would be interested in playing quick games if my play could be tied to my
energy usage, I could conserve energy with successful play, and my savings could be
applied to my bill, or used to support worthy causes.”)
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 57.754 ᵃ 2 ᵃ 28.877 ᵃ 53.365 ᵃ < .001 ᵃ 0.060
RM Factor 1 ✻ AgeCat 6.299 ᵃ 10 ᵃ 0.630 ᵃ 1.164 ᵃ 0.311 ᵃ 0.007
Residuals 899.342 1662 0.541
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
AgeCat 113.357 5 22.671 7.598 < .001 0.044
Residuals 2479.750 831 2.984
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 AgeCat Mean SD N
quick 1 1 4.129 1.002 147
2 4.133 1.090 180
3 4.119 1.072 160
4 4.000 1.130 142
5 3.796 1.297 142
6 3.364 1.495 66
quick 2 1 3.762 1.143 147
2 3.811 1.082 180
3 3.644 1.118 160
4 3.500 1.178 142
5 3.444 1.170 142
6 3.030 1.240 66
quick 3 1 4.007 1.132 147
2 4.017 1.075 180
3 3.731 1.227 160
4 3.690 1.227 142
5 3.549 1.252 142
Gamification-1
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Descriptives
RM Factor 1 AgeCat Mean SD N
6 3.318 1.361 66

Descriptives plots
Gamification-1
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Income Effects
Acronyms for Game Types
Action Games = act
Adventure Games = adv
Role Play Games = rlpl
Simulation Games/Simulators = siml
Strategy Games = strt
Sports Games = sprts
Puzzle Games = pzzl
Word Games = word
Income Categories
1 = Near or below the poverty line: less than $19,999
2 = Low income: $20,000-39,999
3 = Low Middle Income: $40,000-69,999
4 = Middle Income: $70,000-139,999
5 = Upper middle income (and higher): 140,000 and above
Acronyms for Incentive Types
Personal Savings = svngs
Help the Needy = needy
Personal Causes = pcaus
Social Causes = scaus
Educational Content = edcnt
Recreational Content = recr
Online Services = olsrv
Energy Products = prods
Gamification-1
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Age Category  Game Types
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 2505.016 ᵃ 7 ᵃ 357.859 ᵃ 69.388 ᵃ < .001 ᵃ 0.077
RM Factor 1 ✻
IncRankCat 607.531 ᵃ 28 ᵃ 21.698 ᵃ 4.207 ᵃ < .001 ᵃ 0.020
Residuals 30036.723 5824 5.157
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
IncRankCat 0.029 4 0.007 0.795 0.528 0.004
Residuals 7.571 832 0.009
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 IncRankCat Mean SD N
act 1 3.947 2.391 187
2 4.142 2.402 211
3 4.467 2.463 210
4 4.904 2.626 197
5 4.781 2.433 32
adv 1 3.235 1.616 187
2 3.299 1.821 211
3 3.643 1.809 210
4 4.132 1.855 197
5 4.313 1.786 32
pzzl 1 4.123 1.937 187
2 3.991 2.093 211
3 4.319 2.002 210
4 4.569 1.972 197
5 4.750 1.901 32
rlpl 1 4.380 1.973 187
2 4.389 1.998 211
3 4.376 1.931 210
4 4.574 1.767 197
5 4.344 2.026 32
Gamification-1
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Descriptives
RM Factor 1 IncRankCat Mean SD N
siml 1 4.824 1.836 187
2 4.848 1.772 211
3 4.638 2.003 210
4 4.411 2.050 197
5 5.281 1.938 32
sprts 1 6.620 1.603 187
2 6.441 1.847 211
3 6.433 1.722 210
4 6.386 1.768 197
5 6.063 1.722 32
strt 1 3.941 2.478 187
2 4.123 2.353 211
3 3.738 2.391 210
4 3.127 2.204 197
5 3.188 2.669 32
word 1 4.893 2.621 187
2 4.768 2.585 211
3 4.357 2.690 210
4 3.898 2.555 197
5 3.281 2.453 32

Descriptives plots
Gamification-1
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Income Category – “Enjoy playing games”
ANCOVA - EnjPlay
Cases Sum of Squares df Mean Square F p η² p
IncRankCat 4.891 4 1.223 0.834 0.503 0.004
Residuals 1219.104 832 1.465
Note. Type III Sum of Squares

Descriptives
Descriptives - EnjPlay
IncRankCat Mean SD N
1 3.973 1.184 187
2 3.900 1.304 211
3 4.067 1.192 210
4 3.893 1.175 197
5 4.125 1.040 32

Descriptives plots
Gamification-1
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Income Category  “Prefer playing longer games” / “Prefer playing
shorter games”
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 58.027 1 58.027 41.451 < .001 0.047
RM Factor 1 ✻ IncRankCat 16.665 4 4.166 2.976 0.019 0.014
Residuals 1164.694 832 1.400
Note. Type III Sum of Squares

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
IncRankCat 1.531 4 0.383 0.177 0.951 8.480e -4
Residuals 1804.287 832 2.169
Note. Type III Sum of Squares
Descriptives
Descriptives
RM Factor 1 IncRankCat Mean SD N
long 1 3.444 1.407 187
2 3.284 1.469 211
3 3.252 1.417 210
4 3.122 1.452 197
5 2.938 1.625 32
short 1 3.578 1.208 187
2 3.645 1.247 211
3 3.629 1.220 210
4 3.751 1.197 197
5 3.875 1.185 32
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Descriptives plots
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Income Category  “Prefer playing solo” / “Prefer playing opponents”
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 31.818 1 31.818 26.552 < .001 0.031
RM Factor 1 ✻ IncRankCat 4.557 4 1.139 0.951 0.434 0.005
Residuals 996.993 832 1.198
Note. Type III Sum of Squares

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
IncRankCat 4.295 4 1.074 0.490 0.743 0.002
Residuals 1822.200 832 2.190
Note. Type III Sum of Squares
Descriptives
Descriptives
RM Factor 1 IncRankCat Mean SD N
oppnt 1 3.118 1.243 187
2 3.166 1.278 211
3 3.071 1.226 210
4 3.137 1.284 197
5 3.406 1.266 32
solo 1 2.963 1.353 187
2 2.796 1.381 211
3 2.776 1.306 210
4 2.746 1.323 197
5 2.813 1.401 32

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Descriptives plots
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Income Category  Incentive Types
Within Subjects Effects
Cases Sum of
Squares df Mean
Square F p η² p
RM Factor 1 708.412 ᵃ 7 ᵃ 101.202 ᵃ 128.395 ᵃ < .001 ᵃ 0.134
RM Factor 1 ✻
IncRankCat 43.491 ᵃ 28 ᵃ 1.553 ᵃ 1.971 ᵃ 0.002 ᵃ 0.009
Residuals 4590.502 5824 0.788
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
IncRankCat 20.478 4 5.119 0.880 0.475 0.004
Residuals 4837.874 832 5.815
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 IncRankCat Mean SD N
edcnt 1 2.952 1.263 187
2 3.005 1.336 211
3 2.800 1.275 210
4 2.772 1.243 197
5 2.313 1.378 32
needy 1 3.139 1.215 187
2 3.142 1.179 211
3 3.067 1.092 210
4 3.147 1.076 197
5 2.938 1.366 32
olsrv 1 2.807 1.326 187
2 2.910 1.256 211
3 2.910 1.255 210
4 2.802 1.312 197
5 2.531 1.391 32
pcaus 1 3.166 1.140 187
2 3.204 1.130 211
3 3.195 1.151 210
4 3.234 1.048 197
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Descriptives
RM Factor 1 IncRankCat Mean SD N
5 3.063 1.458 32
prods 1 3.551 1.214 187
2 3.706 1.199 211
3 3.695 1.116 210
4 3.655 1.170 197
5 3.563 1.134 32
recr 1 3.037 1.288 187
2 3.071 1.283 211
3 2.895 1.252 210
4 2.868 1.238 197
5 2.500 1.295 32
scaus 1 3.053 1.181 187
2 3.251 1.133 211
3 2.971 1.161 210
4 3.071 1.042 197
5 2.969 1.448 32
svngs 1 3.845 1.156 187
2 4.033 1.084 211
3 4.110 1.018 210
4 4.157 1.000 197
5 4.250 0.916 32

Descriptives plots
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Income Category  Quick 1-2-3
(Quick 3: “I would be interested in playing quick games if my play could be tied to my
energy usage, I could conserve energy with successful play, and my savings could be
applied to my bill, or used to support worthy causes.”)
Within Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
RM Factor 1 42.092 ᵃ 2 ᵃ 21.046 ᵃ 38.970 ᵃ < .001 ᵃ 0.045
RM Factor 1 ✻ IncRankCat 6.991 ᵃ 8 ᵃ 0.874 ᵃ 1.618 ᵃ 0.115 ᵃ 0.008
Residuals 898.650 1664 0.540
Note. Type III Sum of Squares
ᵃ Mauchly's test of sphericity indicates that the assumption of sphericity is violated (p <
.05).

Between Subjects Effects
Cases Sum of Squares df Mean Square F p η² p
IncRankCat 3.134 4 0.783 0.252 0.909 0.001
Residuals 2589.973 832 3.113
Note. Type III Sum of Squares

Descriptives
Descriptives
RM Factor 1 IncRankCat Mean SD N
quick 1 1 3.925 1.189 187
2 3.919 1.194 211
3 4.005 1.151 210
4 4.117 1.126 197
5 3.938 1.294 32
quick 2 1 3.620 1.127 187
2 3.640 1.131 211
3 3.581 1.180 210
4 3.558 1.153 197
5 3.438 1.480 32
quick 3 1 3.759 1.196 187
2 3.848 1.161 211
3 3.705 1.225 210
4 3.797 1.203 197
5 3.594 1.583 32

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Descriptives plots
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APPENDIX D
Additional Relevant Literature
Note: This is a working document that will likely expand (and be elaborated on) over
time.
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Additional Relevant Literature
Aguirre-Bielschowskya, I., Lawson, R., Stephenson, J., & Todd, S. (2018). Kids and
Kilowatts: Socialisation, energy efficiency, and electricity consumption in New Zealand.
Energy Research and Social Science, 44, 178-186.
Socialisation into electricity consumption usually occurs during childhood, but little is known
about the socialization processes involved. Here, we use interviews and focus groups to
investigate how nine to ten-year-old children from New Zealand learn about, and consume,
electricity in their homes. The children used a wide range of electrical appliances and engaged in
different energy saving behaviours, often without being conscious of their implications. Control
over appliances and learning through modelling, reminders and rules helped to socialize children
into saving electricity, while nagging and inconsistent behaviours from parents were
counterproductive. Conversations about energy were uncommon, but helpful for creating
consciousness about energy use. We discuss the need for a more structured approach, through
developing energy literacy, in order for children to use their agency, surpass their parents’ level
of energy saving practices, and stabilise energy saving behaviours through life. In addition, we
provide recommendations on how parents, schools, the media and product developers can help in
this process.
Al Skaif, T., Lampropoulos, I., van den Broek, M. & van Sark, W. (2018). Gamification-
based framework for engagement of residential customers in energy applications. Energy
Research and Social Science, 44, 187-195.
According to the European Union Third Energy Market package, the roll-out of smart meters in
the residential sector can presumably play a key role in reaching the goals of sustainability
strategies. However, the deployment of smart meters alone does not necessarily drive residential
customers to use energy in a more sustainable manner. Therefore, more attention should be paid
to customers energy behavior in order to reach the objectives of the roll-out policy. In this study,
we propose an interdisciplinary framework that establishes a behavioral model to identify the
main energy-related behavior change requirements necessary to engage residential customers in
energy applications. To fulfill the requirements, we first present the technical system architecture
that enables energy applications for residential customers. Then, we assess how gamification,
which is the employment of game design elements in non-game contexts, can be used to enhance
energy applications by driving customer engagement and energy-related behavior change. To do
that, the most relevant game design elements are discussed and classified. After that, the expected
value streams from using a gamification-based solution for different stakeholders in the energy
market are identified. Finally, the study discusses the potential of the proposed framework in
different energy applications for residential customers.
Andrew T. Miranda, A. T & Evan M. Palmer, E. M (2014). Intrinsic motivation and
attentional capture from gamelike features in a visual search task. Behavior Research
Methods, published online July 9, 2013.
In psychology research studies, the goals of the experimenter and the goals of the participants
often do not align. Researchers are interested in having participants who take the experimental
task seriously, whereas participants are interested in earning their incentive (e.g., money or
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coursecredit) as quickly as possible. Creating experimental methods that are pleasant for
participants and that reward them for effortful and accurate data generation, while not
compromising the scientific integrity of the experiment, would benefit both experimenters and
participants alike. Here, we explored a gamelike system of points and sound effects that rewarded
participants for fast and accurate responses. We measured participant engagement at both
cognitive and perceptual levels and found that the point system (which invoked subtle,
anonymous social competition between participants) led to positive intrinsic motivation, while the
sound effects (which were pleasant and arousing) led to attentional capture for rewarded colors.
In a visual search task, points were awarded after each trial for fast and accurate responses,
accompanied by short, pleasant sound effects. We adapted a paradigm from Anderson, Laurent,
and Yantis (Proceedings of the National Academy of Sciences 108(25):10367-10371, 2011b), in
which participants completed a training phase during which red and green targets were
probabilistically associated with reward (a point bonus multiplier). During a test phase, no points
or sounds were delivered, color was irrelevant to the task, and previously rewarded targets were
sometimes presented as distractors. Significantly longer response times on trials in which
previously rewarded colors were present demonstrated attentional capture, and positive responses
to a five-question intrinsic-motivation scale demonstrated participant engagement.
Bang, M., Anton Gustafsson, A. and Cecilia Katzeff, C. (2007). Promoting New Patterns in
Household Energy Consumption with Pervasive Learning Games. In Y. de Kort et al.
(Eds.), Persuasive 2007, Lecture Notes in Computer Science, 4744, 55–63.
game.
Relevance/Conclusions: The authors propose a game interface to customer usage (a single game:
PowerAgent). The game is described, but never developed and implemented. PowerAgent would
interact with smart meters. Similar to our proposal, but PowerAgent is a closer metaphorically to
actual power management. Likely not implemented because the authors had the same issue we
did: How to connect to the data stream. (RR)
Deterding, S. (2011). Situated motivational affordances of game elements: A conceptual
model. CHI Conference, May 7–12, Vancouver, BC, Canada.
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011, September). From game design
elements to gamefulness: defining" gamification". In Proceedings of the 15th international
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academic MindTrek conference: Envisioning future media environments (pp. 9-15).
Recent years have seen a rapid proliferation of mass-market consumer software that takes
inspiration from video games. Usually summarized as “gamification”, this trend connects to a
sizeable body of existing concepts and research in human- computer interaction and game studies,
such as serious games, pervasive games, alternate reality games, or playful design. However, it is
not clear how “gamification” relates to these, whether it denotes a novel phenomenon, and how to
define it. Thus, in this paper we investigate “gamification” and the historical origins of the term in
relation to precursors and similar concepts. It is suggested that “gamified” applications provide
insight into novel, gameful phenomena complementary to playful phenomena. Based on our
research, we propose a definition of “gamification” as the use of game design elements in non-
game contexts.
Relevance/Conclusions: Authors seek to further delineate the concept gamification from other
closely related concepts, such as: gamefulness, playfulness, and gameful design. May be relevant
for our team as we document our process and operationally refer to similar concepts. (KP)
CHI, 2010, April. (note: CHI is the premier conference for research
and practice related to Human-Computer Interaction. This paper was part of a
symposium.)
Eco-feedback technology provides feedback on individual or group behaviors with a goal of
reducing environmental impact. The history of eco-feedback extends back more than 40 years to
the origins of environmental psychology. Despite its stated purpose, few HCI eco-feedback
studies have attempted to measure behavior change. This leads to two overarching questions: (1)
what can HCI learn from environmental psychology and (2) what role should HCI have in
designing and evaluating eco-feedback technology? To help answer these questions, this paper
conducts a comparative survey of eco-feedback technology, including 89 papers from
environmental psychology and 44 papers from the HCI and UbiComp literature. We also provide
an overview of predominant models of proenvironmental behaviors and a summary of key
motivation techniques to promote this behavior.
Relevance/Conclusions: Directly relevant with respect to the importance of feedback, but does
not deal with interactive systems, nor with the role of incentives. (RR)
Journal of Design Research, 10, 103-120.
This paper presents a study called the Energy Battle, a game aimed at encouraging home
occupants to save energy. Twenty student-households were provided with direct feedback and an
online platform with energy feedback over time, ranking of the competing teams, tips and a game.
The study showed that the game context strongly influenced the motivation to save energy.
Overall, savings averaged 24%, with the highest savings level at 45%. Directly after completion
of the Energy Battle, energy consumption increased among most of the households, although
consumption levels tended to stay below the baseline measurement level taken before the Energy
Battle. Follow-up interviews indicated that some of the behaviours developed in the game had
transformed into habits. A game such as the Energy Battle appears to provide a powerful means
to stimulate energy saving in the short term. The potential to achieve long term effects appears
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possible, however further research is required to understand long-term implications for an Energy
Battle game.
Guang Shi a, V., Baines, T., Baldwin, J., Ridgway, K., Petridis, P., Ziaee Bigdeli, A., Uren,
V. & Andrews, D. (2015). Using gamification to transform the adoption of servitization.
Industrial Marketing Management, 63, 82-91.
Hardin, D. (2011). Customer Energy Services Interface White Paper SmartGrid
Interoperability Panel B2G/I2G/H2G Domain Expert Working Groups Editor - Dave Hardin,
EnerNOC.
The Energy Services Interface (ESI) is a concept that has been identified and defined within
a number of smart grid domains (ref. NIST Conceptual Model1). Within these domains, an
ESI performs a variety of functions. The purpose of this paper is to focus on the ESI at the
customer boundary and provide a perspective that will aid in developing a common
understanding and definition of the ESI through a review of use case scenarios,
requirements and functional characteristics.
He, H. A., & Greenberg, S. (2008). Motivating sustainable energy consumption in the home.
University of Calgary.
Relevance/Conclusions: This document is essentially a thought paper that describes ideas for
applying psychological theory to visualizations that represent energy usage related feedback for
customers. However, the ideas are not very developed at this juncture and probably offer little in
terms of use within our own gamification. (KP)
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Heeter, C., Lee, L, Medler, B., & Magerko, B. (2011). Beyond player types: Gaming
achievement goal. Proceedings of the Annual Meeting of the Association for Computer
Machninery. New York.
Education and psychology studies have used motivational constructs called achievement goals to
predict learning success and response to failure. In this article we adapted classroom achievement
goal scales to instead measure gaming achievement goals. We collected survey data from 432
university students to empirically examine the applicability and utility of achievement goal
constructs from education research to gaming. We introduced a new approach to player types
based on mastery and performance gaming achievement goals. Four types are studied:
superachievers, mastery-only, performance-only, and non-achievers. We also examined the
relationship between our four achievement goal player types to the traditional achiever, explorer
player types. We found that Interest in exploration in games can exist in any of the four types, but
those with strong mastery goals have the lowest interest in exploration. Gender and gaming
frequency were significantly related to gaming achievement goals. The implications and
suggestions for designing games for learning and entertainment are discussed.
Education, 33, 558-574.
Because engineering faculty seldom use research-based instructional strategies, the engineering
education community has become increasingly concerned with how to help faculty sustainably
integrate education research into their teaching practices. We developed the Intrinsic-Motivation
(IM) Course Design Method to make motivation theory accessible to faculty and to help faculty
think more concretely about the costs that demotivate them and make their course designs
untenable. Our course design method complements existing course design methods by providing
an approach to designing for motivational outcomes. In this paper, we describe the IM Course
Design Method and then illustrate how this method was used to refine the design of a sophomore-
level engineering course that enrolled over 200 students. We then present evaluation evidence
from this course to suggest that application of the method can increase students’ intrinsic
motivation in engineering courses.
Renewable and
Sustainable Energy Reviews, 73, 249-264.
Energy consumption is a significant and critical social issue. Gamification and serious games
offer a means of influencing people regarding energy consumption. A systematic review of
articles (written in English) was conducted according to the specifications of the PRISMA
checklist, in order to examine the literature and assess empirical support for the effectiveness of
gamification and serious games in impacting domestic energy consumption. The search strategy
included a combination of terms relating to gamification and serious games, and domestic energy
consumption. Only primary studies reporting empirical data relating to the value of gamification
and serious games on energy consumption were included. More comprehensive selection criteria
were applied throughout the selection process (reported in full in the main text). Twenty-five
primary studies published in 26 research articles were included in the final review. The findings
indicate that gamification and serious games appear to be of value within the domain of energy
consumption, conservation and efficiency, with varying degrees of evidence of positive influence
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found for behaviour, cognitions, knowledge and learning and the user experience. A common
feature across many articles reviewed was the limited amount and quality of empirical evidence,
which suggests that more rigorous follow-up studies are required to address this gap. The article
makes specific recommendations to help address this challenge.
Relevance/Conclusions: This is a review paper. Authors conclusions: ‘It can be concluded that
applied games generally provide a positive user experience with all studies reporting positive
impacts on this outcome. With respect to cognitive outcomes, there is consistent evidence of
improved attitudes towards and awareness of energy conservation issues. In almost all studies,
applied games appear to lead to improvements in self-reported and actual energy conservation
behaviour but it is not yet clear whether these changes persist long- term. With respect to
knowledge and learning, applied games appear to be effective means of improving general
knowledge of energy consumption and conservation, but it is less clear that they are effective for
communicating more specific knowledge. Overall, there is emerging evidence of the value of
applied games as a means of reducing domestic energy consumption with further research needed
to answer key outstanding questions’. The outstanding questions, brought forth by their review,
are the important questions we are seeking to address in an applied fashion with our own work;
e.g. institute long-term behavioral change and communicate specific knowledge related to energy
consumption. (KP)
Kim, Amy Jo (2012). Social Engagement: who’s playing? how do they like to engage? Self
published: https://amyjokim.com/blog/2012/09/19/social-engagement-whos-playing-how-do-
they-like-to-engage/.
Koivisto, J. & Hamari, J. (2014). Demographic differences in perceived benefits from
gamification. Computers in Human Behavior, 35, 179-188.
Kumar , J. M. and Herger, M (2013). Gamification at Work: Designing Engaging Business
Software. Esp. Chapter 5: Motivation. Interaction Design Foundation.
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Understanding human motivation is an important part of creating an effective gamification
strategy. Students of gamification could benefit from studying the existing research on
motivation. In this chapter, we have curated a list of motivational drivers that we believe are a
good place to start. They are collecting, connecting, achievement, feedback, reciprocity, and
blissful productivity.
Lee, B. W. & Leeson, P. R. C. (2015). Online gaming in the context of social anxiety.
Psychology of Addictive Behaviors, 29 (2), 473-482.
In 2014, over 23 million individuals were playing massive multiplayer online role-playing games
(MMORPGs). In light of the framework provided by Davis's (2001) cognitive-behavioral model
of pathological Internet use, social anxiety, expressions of true self, and perceived in-game and
face-to-face social support were examined as predictors of Generalized Problematic Internet Use
Scale (GPIUS) scores and hours spent playing MMORPGs per week. Data were collected from
adult MMORPG players via an online survey (N = 626). Using structural equation modeling, the
hypothesized model was tested on 1 half of the sample (N = 313) and then retested on the other
half of the sample. The results indicated that the hypothesized model fit the data well in both
samples. Specifically, expressing true self in game, higher levels of social anxiety, larger numbers
of in-game social supports, and fewer supportive face-to-face relationships were significant
predictors of higher GPIUS scores, and the number of in-game supports was significantly
associated with time spent playing. The current study provides clinicians and researchers with a
deeper understanding of MMORPG use by being the first to apply, test, and replicate a theory-
driven model across 2 samples of MMORPG players. In addition, the present findings suggest
that a psychometric measure of MMORPG usage is more indicative of players' psychological and
social well-being than is time spent playing these games.
Lee, E., Gadh, R., and Gerla, M. (2013). Energy Service Interface: Accessing to customer
energy resources for smart grid interoperation. IEEE Journal on Selected Areas in
Communications, 31(7), 1-10.
The Energy Service Interface (ESI), sitting at the boundary of a customer facility, plays a
communication gateway role - interconnecting internal customer energy resources to external
systems. A number of customer energy services are realized over the interconnected
communications, which then contributes to smart grid interoperation eventually. In this paper, we
examine the design issues of the ESI. To facilitate bidirectional customer energy services, the ESI
must serve as both a service consumer and a service provider. At the same time, it must protect
the customer energy resources from external threats and maximize the interoperation. To verify
the issues, we build and deploy two ESI testbeds. Throughout experiments with a couple of
energy service scenarios, we verify that the ESI plays the service “prosumer” in an interoperable
manner. We also evaluate the performance of the security mechanism applied to the ESI and
examine the potential of exploiting the Cloud technology for the ESI deployment. To the best of
authors’knowledge, this is the first deployment of the ESI that addresses the fundamental,
functional requirements.
Leftheriotis, J., Giannakos, N. & Jaccheri, L. (2017). Gamifying informal learning activities
using interactive displays: an empirical investigation of students’ learning and Engagement.
Smart Learning Environments, 4, 2.
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Interactive displays (IDs) are increasingly employed in informal learning environments, where
they are seen as a medium for enhancing students’ creativity, and engagement. Due to the larger
space they provide and thus the larger interaction area, they allow for group-work, working in
parallel, co-creating artifacts or co-experiencing the interaction in a playful manner. In particular,
gaming activities in IDs enhance students’ mental exercise and fantasy and promote students
engagement through rewards and collaboration. However, despite the increased prevalence of
interactive displays and gamification, we know very little about how designers and instructors can
gamify their learning activities by taking advantage of the IDs. In this paper, a framework for
developing gamified activities for interactive displays is presented. For the empirical evaluation,
pre-post attitudinal surveys and cognitive tests along with photos and observations were recorded
and used. The contribution of this article is twofold: 1) an adaptable framework for developing
gamified activities on interactive displays (GAID), and 2) the results of a field study where
students have been engaged with an interactive display application during an extracurricular
activity. By incorporating GAID to a traditional informal learning activity, it is found that
students’ knowledge acquisition, satisfaction, enjoyment and intention to participate on similar
events in the future are significantly improved.
Lim, S. & Jong-Eun, R. (2009). When playing together feels different: Effects of task types
and social contexts on physiological arousal in multiplayer online gaming contexts.
CyberPsychology and Behavior, 12, 59-61.
This study examines how task types (violent vs. nonviolent) and social contexts (solo vs.
collaborative) affect physiological arousal in multiplayer online gaming. Our results show that
social contexts modify the effects of violent game tasks on arousal. When compared with solo
play, collaborative play led to a significant decrease in arousal in response to violent tasks, while
leading to a slight increase for nonviolent tasks. The findings point to the importance of
understanding how social contexts of game playing shape psychological experiences in
multiplayer online games.
Loeb, L., Loeb, G., Tice, E., & Tregubov,T. (2010). Emotionally Engaging Students to
Change Behaviors and Conserve Resources: Unplug or the Polar Bear Gets It!
Our goal is to make an emotional connection between individual energy use and its impact on the
environment in order to motivate people to change their behavior and conserve resources.
TellEmotion’s unique method for displaying real-time energy use information combines research
in the areas of behavioral sciences, sociology, information visualization, computer science and
interaction design to motivate energy efficiency. In its simplest form, we begin with an animated
polar bear. When energy use is low, the bear is happy; when it is high, the health and happiness of
the bear is endangered. In this paper we present recent findings from implementations in two
school settings: Brooks School (a boarding high school in N. Andover, MA) and Dartmouth
College (in Hanover, NH) during the spring term of 2009. Electricity reduction from the
implementation of TellEmotion’s GreenLite System, averaged between 10 and 11% for a six-
week period, with one dorm in each school reducing electricity use by as much as 34%. In both
settings, students quickly “fell in love” with the bear, named her (“Bula” at Dartmouth and
“Pasha” at Brooks), sent emails to their friends to turn things off to save the bear when energy use
was high and remained motivated and engaged over the length of our trials, with reductions even
improving over time at Brooks. There were no other incentives in place to encourage students to
reduce electricity use and no physical energy efficiency improvements made during the trial
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period.
Relevance/Conclusions: Directly relevant with respect to the importance of feedback, gives
customers actionable information (and measures actions), but leaves no role for incentives. (RR)
Mekler, E. D. (2015). The Motivational Potential of Digital Games and Gamification – The
Relation between Game Elements, Experience and Behavior Change. Unpublished doctoral
dissertation, University of Basel.
Meuer, M., Middelhoff, J., Segorbe, J., and Vollhardt, K.(2020). How energy companies can
tap into marketing personalization. McKinsey & Company Internal Document.
---In this
challenging environment, their top priority is to keep customers from churning. Long-term
customers with old contracts can be many times more valuable than newer customers, and
winning new customers is a costly undertaking. Fortunately, today’s companies have a new tool
at their disposal to help them retain old customers and acquire new ones: personalization. By
adopting automated algorithm-driven processes to tailor appeals to individual customers based on
their behavior, companies can manage their customer base more effectively and prevent churn.---
Some energy companies have started experimenting with simple forms of personalization, such as
splitting their customer base into two subsets to test two different messages for a price-adjustment
campaign. However, most companies still send out identical messages to all their customers.
Although developing personalization capabilities may seem daunting at first, getting started is
more manageable than it seems—and it gives companies a real edge in winning, managing, and
retaining customers. Our research shows that companies that provide effective personalized
content can increase sales by 10 percent or more, as well as delivering five- to eightfold
improvements in their return on marketing spending.
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Morgantia,L, Pallavicinia, F. Cadela, E., Candelierib, A., Archettib, F. & Mantovania, F.
(2017). Gaming for Earth: Serious games and gamification to engage consumers in pro-
environmental behaviours for energy efficiency. Energy Research and Social Science, 29, 95-
102.
Serious games and gamification to engage consumers in pro-environmental behaviours for energy
efficiency are in their infancy. To date, despite growing interest and some initial attempts, their
potential to engage consumers in energy efficiency behaviours has been understudied. To provide
an overview of serious games and gamification to engage individuals in pro-environmental
behaviours for energy efficiency, a computer-based search for relevant publications was
performed in several databases. After applying the inclusion criteria and removing duplicates, 10
papers were included in this systematic review. Results showed that serious games and
gamification have been used in three different areas related to energy efficiency: environmental
education, consumption awareness, and pro-environmental behaviours. This review also showed
that applied gaming interventions can be used in more than one of these three areas
(comprehensive interventions). The main observation to be drawn from this review is that both
serious games and gamification can foster energy-saving behaviours and vary widely in terms of
type of games and of features that might be appealing and motivating.
Gamification in Education and Business, New York: Springer.
Meaningful gamification is the use of gameful and playful layers to help a user find personal
connections that motivate engagement with a specific context for long-term change. While
reward-based gamification can be useful for short-term goals and situations where the participants
have no personal connections or intrinsic motivation to engage in a context, rewards can reduce
intrinsic motivation and the long-term desire to engage with the real world context. If the goal is
long-term change, then rewards should be avoided and other game-based elements used to create
a system based on concepts of meaningful gamification. This article introduces six concepts -
Reflection, Exposition, Choice, Information, Play, and Engagement - to guide designers of
gamification systems that rely on non-reward-based game elements to help people find personal
connections and meaning in a real world context.
Hypothesis and Theory, 9, 1-15.
Research in psychology has shown that both motivation and wellbeing are contingent on the
satisfaction of certain psychological needs. Yet, despite a long-standing pursuit in human-
computer interaction (HCI) for design strategies that foster sustained engagement, behavior
change and wellbeing, the basic psychological needs shown to mediate these outcomes are rarely
taken into account. This is possibly due to the lack of a clear model to explain these needs in the
context of HCI. Herein we introduce such a model: Motivation, Engagement and Thriving in User
Experience (METUX). The model provides a framework grounded in psychological research that
can allow HCI researchers and practitioners to form actionable insights with respect to how
technology designs support or undermine basic psychological needs, thereby increasing
motivation and engagement, and ultimately, improving user wellbeing. We propose that in order
to address wellbeing, psychological needs must be considered within five different spheres of
analysis including: at the point of technology adoption, during interaction with the interface, as a
result of engagement with technology-specific tasks, as part of the technology-supported
Gamification-1
101
behavior, and as part of an individual’s life overall. These five spheres of experience sit within a
sixth, society, which encompasses both direct and collateral effects of technology use as well as
non-user experiences. We build this model based on existing evidence for basic psychological
need satisfaction, including evidence within the context of the workplace, computer games, and
health. We extend and hone these ideas to provide practical advice for designers along with real
world examples of how to apply the model to design practice.
Ryan, D. Intrinsic Motivation Inventory (no date; this paper unpublished).
Santin, O. G. (2011). Behavioural patterns and user profiles related to energy consumption
for heating. Energy and Buildings, 43(10), 2662-2672.
Relevance/Conclusions: Although somewhat interesting, they did not establish any relationships
between energy consumption and their variables. Little relevance for our project. (KP)
Meyer, T., Soebech, O., & Shahin, J. (2018). Keeping it real. Lessons on implementing
sustainable consumption policy at a local level. Paper presented at the IAMCR 2018
Conference, Eugene, Oregon, USA, June 20-24.
Gamification-1
102
techniques. Building on insights from literature on technological acceptance (Venkatesh et al.
2003) and behavioural change (Stern 2005) we have sought to embed the energy monitor in an
information-rich, community-centred and enabling environment. We hypothesise that the
technology will provide participants with insight into their consumption patterns, but will not lead
to sustainable lifestyle changes in or of itself.
Various (current). EnerGAware. European energy awareness project:
http://www.energaware.eu/.
will develop and test, in 100
affordable homes, a serious game that will be linked to the actual energy consumption (smart
meter data) of the game user’s home and embedded in social media and networking tools.
Various (current). GreenPlay. Another European energy awareness project:
http://www.greenplay-project.eu/.
Various (current). Treasure Hunt Entropy. Online game at https://arvrtech.eu/treasure-
hunt-entropy/.
Wemyssa,, D., Cellinac, F., Lobsiger-Kägib, E., de Lucad, V., & Castric, R. (2018). Does it
last? Long-term impacts of an app-based behavior change intervention on household
electricity savings in Switzerland. Energy Research and Social Science, 47, 16-27.
Gamification-1
103
cities were actively involved and monitored along with corresponding control group of forty
households. The intervention engaged app users in a neighborhood challenge to complete
electricity saving activities and realize their progress through electricity use visualization. One
year after the intervention, electricity consumption was measured, and follow-up online surveys
measured reported behavior and perceived injunctive norms of the participants. During the
intervention, participants significantly reduced their electricity use, with respect to both historical
consumption and the control groups. However, after one year it was found that the electricity
savings achieved during the intervention were not maintained. In contrast, the participants
reported their behavior as more efficient compared to before the intervention and still perceived
the impact of the intervention in their community. This counter intuitive relation between the
three measured variables is discussed, along with possible strategies to maintain the positive
effects achieved in the short-term.
Zammitto, V. (2009). Game research, measuring gaming preferences. Future Play, GDC
Canada (Preprint).
APPENDIX F
Final Report: Energy Trading Phase II
Avista Transactive Power: Final Project Report of Phase II
Center for Secure and Dependable Systems,
University of Idaho
Abstract
We have developed a prototype software system with the objectives of supporting the creation and
management of a market that enables prosumers and consumers to trade electric power between
themselves or with the utility, with utility oversight. This prototype software system supports the
creation and management of electric power transaction agreements between prosumers, integrating
power flow analysis and calculating distribution locational marginal prices (DLMP). The proposed
prototype enables the study of approaches to create a transactive energy market while ensuring a
feasible, secure, and economical distribution grid operation.
Phase I Developed Work
At the end of phase I, we completed the analysis, design, and implementation of prototype software
that integrates energy market management and a power flow analysis. This prototype supports the
creation and management of prosumer-enabled transaction intents and determines whether such
transactions could be supported by a distribution grid model based on voltage levels. Results of
the voltage feasibility analysis were used to enable/disable transactions on the market application.
The Avista Transactive Power Application (ATPA) prototype system architecture developed
consists of four modules. These are: 1) Distribution System Model and OpenDSS Simulation, 2)
Web-based Management Interface; 3) Database; and 4) Communications Manager [1].
We used a distributed renewables-enhanced 13-bus system model with added realistic and hourly
configurable load and generation profiles. This system fully supported voltage-based energy
transaction feasibility analysis. The details of the power system model and the ATPA modules are
available in our final report of phase I [1].
Phase II Obtained Results
Customer-initiated energy transaction prioritization and pricing
We have enhanced the prototype during phase II and integrated it with an algorithm for energy
price calculation. This algorithm calculates the Distribution Locational Marginal Pricing (DLMP)
for each bus in the system and determines dispatch schedules for a dispatchable generation. The
estimated power flow, dispatch schedules, and DLMPs are calculated after all information from
the prosumer's usage, generation profiles and all transaction intents have been considered within
each hourly window and for any selected time window.
The system prototype has also been enhanced with a transaction intent prioritization algorithm that
enables the selection of transactions based on priority and the DLMP price, in addition to voltage
feasibility. Transactions are enabled/disabled depending on voltage, DLMP results, and
transaction given priority.
Distribution Locational Marginal Pricing (DLMPs)
Avista Transactive Power Application
script. Secondly, the documentation specifies the names and uses of function parameters in the
script and the organizational structure of the script itself. The function parameters are detailed
inside lengthy comments below the function signatures used by each documented function. The
Sphinx documentation system handles displaying the script's organizational structure in terms of
the files the script uses.
When an overvoltage is detected due to transaction activity, the most recent version of the ATPA
backend works to correct the issue. The ATPA script will incrementally disable transactions, one
at a time until the system has no longer overvoltages. Transactions are disabled in order from those
with the smallest priority value to the greatest. Figure 1 shows the current architecture of the
ATPA. Figure 2 details the ATPA functionality:
Enhanced 13-bus system case study:
One case study, based on a renewable enhanced IEEE 13-bus model, has been developed in phase
I and was upgraded in phase II. Scenarios include a full distribution system model (IEEE 13-bus),
classic and renewable distributed generation, different hourly generation and loads profiles, and
example transaction intents. Three scenarios are discussed as follows.
The first scenario has no solar power generation. The power model includes dispatchable
generation at a lower rate than the utility's cost (7 cents/kWh). These generators input their
maximum power at 50kW and 250kW. The rest of the customers' bulk consumption is supplied at
the cost of 11 c/kWh from the primary substation (bus 632) connected to the feeder. Table 1 and
Table 2 show the real and reactive generator dispatch details and the loads for all customers.
Generator Location Kw Kvar
632.A 231.61 3.621
632.B 210.55 167.86
632.C 270.67 134.04
634.A 50 0
634.B 50 0
634.C 50 0
675.A 250 0
675.B 250 0
675.C 250 0
Table 1: Generator Dispatch Full Load Scenario
Customer Kw Kvar
C1 170 20
C2 160 115
C3 120 109
C4 120 240
C5 170 125
C6 0 0
C7 128 68
C8 8.5 5
C9 33 19
C10 58.5 34
C11 0 0
C12 485 -10
C13 68 -140
C14 290 12
Table 2: Customer Load Full Load Scenario
Most of the power has a marginal cost higher than 11 c/kWh as illustrated in Fig.3. These real
power DLMPs are due to the dispatch, line losses, and congestion that the power faces. The same
statement is true for reactive power.
Figure 3. Real power DLMP at different buses of the IEEE 13 bus system.
Figure 4. Reactive power DLMP at different buses of the IEEE 13 bus system.
Scenario 2: PV generation included in the IEEE 13 bus system
The second case considers the use of photovoltaics generation into the IEEE 13-bus system. The
PV rooftop generation is assumed to be installed in residential areas to produce real power. The
total power penetration of PV was set to 60% of the residential loads. The industrial load at bus
675 (C11-13) consumes all of its power. A total of 512 kW PV generation was added to the system.
Thus, the total load supplied by the classical dispatchable generation reduces from 1811kW to
1290kW. With this amount of generated PV power, Table 3 shows that generator 632 on phase A
is no longer producing any real power. This primarily means that, in phase A, real power is not
purchased from the substation feeder. The PV and dispatchable generation fully cover the
consumed power within phase A.
Fig. 5 displays the DLMPs in phase A, which are around the same cost as the dispatchable
generation. Fig 6 indicates the reactive DLMPs where the generators and the PVs are assumed to
provide only real power. The DLMPs values are close to the cost of reactive power at the feeder
11 c/kVARh.
Generator Location Kw Kvar
632.A 0 46.758
632.B 78.487 182.46
632.C 225.17 160.78
634.A 28.441 0
634.B 50 0
634.C 50 0
675.A 250 0
675.B 250 0
675.C 250 0
Table 3: Dispatchable Generation with 60% PV generation.
Customer Kw Kvar
C1 68 20
C2 64 115
C3 48 109
C4 48 240
C5 68 125
C6 0 0
C7 51.2 86
C8 8.5 5
C9 33 19
C10 58.5 34
C11 0 0
C12 485 -10
C13 68 -140
C14 290 12
Table 4: Customer Load with 60% PV generation.
Figure 5. Real power DLMP at different buses of the IEEE 13 bus system with 60% PV.
Figure 6. Reactive power DLMP at different buses of the IEEE 13 bus system with 60% PV.
Scenario 3: PV generation and reactive power injection included in the IEEE 13 bus system
The third scenario combines generation from PV and reactive power from an industrial load, i.e.,
customer 12. The PV contribution is set at 60%, of the total load. The reactive power injection
from customer 12 is increased by 40 kVAR. The reactive power is available in phase A in excess
and is consumed by the feeder substation, as shown in Table 5.
Fig. 7 displays the real DLMPs in phase A, which are around the same cost as the dispatchable
generation. Fig 8 shows the reactive DLMPs, which are negative on phase A due to excess in
reactive power generation on that phase. The reactive DLMP at the bus 632.A is negative due to
the sizeable reactive power injection. Since the real power injections into the feeder are unchanged,
the DLMP price for real power is close to values obtained for scenario 2. Notice that the reactive
power price is close to the substation feeder price, with a negative value obtained from the
optimization constraints on acceptable voltages.
Generator Location Kw Kvar
632.A 0 -2.921
632.B 78.236 182.89
632.C 225.64 161.6
634.A 50 0
634.B 50 0
634.C 50 0
675.A 228.56 0
675.B 250 0
675.C 250 0
Table 5: Dispatchable Generation with 60% PV real power and 40 kVAR injected from an
industrial load.
Customer Kw Kvar
C1 68 20
C2 64 115
C3 48 109
C4 48 240
C5 68 125
C6 0 0
C7 51.2 86
C8 8.5 5
C9 33 19
C10 58.5 34
C11 0 0
C12 485 -50
C13 68 -140
C14 290 12
Table 6: Customer Load with 60% PV generation and 40 kVAR injected from an industrial load.
Figure 7. Real power DLMP at different buses of the IEEE 13 bus system with 60% PV and 40
kVAR reactive power support.
Figure 8. Reactive power DLMP at different buses of the IEEE 13 bus system with 60% PV and
40 kVAR reactive power support.
Conclusion
We describe the phase II improvements and additions to the phase I market software prototype. A
transaction prioritization and nodal pricing algorithm based on DLMPs for both real and reactive
power are considered. The goal is to maximize the economic benefit, welfare, security and
availability of the electric power grid in the presence of prosumers. Scenarios considering different
conditions of the IEEE 13-bus distribution system were illustrated. An analysis scenario, based on
the larger IEEE 34-Bus model, is currently being developed and implemented. Smart building
management systems' interactions with the nodal prices are being investigated. Richer scenarios
are being developed on the IEEE 34-bus system, including DLMP calculation and demand
response.
[1] Avista Transactive Power: Phase I: Final Project Report, 2019.
[2] F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn. Spot Pricing of Electricity. The Kluwer
International Series in Engineering and Computer Science, Power Electronics & Power Systems, Springer
US, Boston, MA, 1988
[3] O. W. Akinbode, A Distribution-class Locational Marginal Price (DLMP) Index for Enhanced
Distribution Systems, M.S thesis, Arizona State University, 2013
[4] S. Hanif, M. Barati, A. Kargarian, H. B. Gooi, and T. Hamacher, "Multiphase Distribution Locational
Marginal Prices: Approximation and Decomposition," 2018 IEEE Power & Energy Society General
Meeting (PESGM), Portland, OR, 2018, pp. 1-5, doi: 10.1109/PESGM.2018.8585925.
[5] Spinx, available online: https://www.sphinx-doc.org/en/master/ ).
APPENDIX G
Two-Page Report: Gamification of Energy Use
Feedback Phase II
Gamification of Energy Use Feedback- Phase 2
Project Duration: 12 months Project Cost: Total Funding $63,483
OBJECTIVE
The objective of the project is to create and
test a gamification system that will motivate
utility customers to attend to their energy
usage data. This attention turns energy usage
into a feedback system in which usage is
viewed as a performance problem. We
assumed, and the literature suggests, that
conservation is a generally held value. When
given the opportunity to conserve, and
information that tells them that they are, or
are not conserving, people will act to conserve
(sometimes called the “Prius Effect”).
Feedback systems require available actions;
our system offers those actions.
Gamification is an inexpensive way to
encourage conservation behaviors by
stimulating greater attentiveness to energy
use data. Moreover, in Phase 1 we discussed
some side benefits; those are clearer now. Our
system offers the opportunity to educate
customers (through tips, videos, etc.) to make
product recommendations, to direct self-
audits, and to contribute to
branding/marketing efforts. Side benefits can
be nudged through the action sets offered.
The needs we noted in Phase 1 are still
pressing: There are increasing (and sometimes
unpredictable) demands on energy, as well as
increasing costs. A utility will benefit when
customers monitor their energy usage more
carefully and more often. As meter systems
become “smarter”, information available to
customers is already becoming more granular
(both in terms of time intervals and, soon, in
terms of individual appliances and devices)
and thus more actionable. With appropriate
direction and motivation, we believe customers
will take actions that lead to optimization of
their usage. A highlight of our project is that
customers will be explicitly aware of the
benefits to themselves as well as the utility
(and society as a whole).
BACKGROUND
The information contained in this document is proprietary and confidential.
of choice for most customers. We will build our
system around that device.
SCOPE
Task 1: --- In order to formally test users
(customers), we completed the Human
Subjects review process that is required by
every university. Avista itself has customer
privacy protections in place. We met both
standards (the university review is complete
and approval is in place).
Task 2: -- We need to identify a sample of
customers that will agree to be tested. This
will require access to the Avista Customer
Experience system. We have a screening
process in place for that moment when access
is granted, and will identify a testing sample
soon afterward.
Task 3: -- We will continue to user test the
aesthetics and playability of the two games
developed in Phase 1. As we add a third
game, it too will be tested. This testing can be
performed with samples of convenience and
does not rely on access to the Avista
Customer Experience System.
Task 4: Overall customer testing of the entire
system will take place. A testing protocol will
be developed. Our intent during Phase 1 was
to conduct testing in person at an Avista
facility. The pandemic has forced us to shift to
online user testing. We are using remote
testing software.
Task 5: A major function of research
universities is to disseminate the results of
the research. We committed to publication of
what we learned about gaming, about
incentives, about smart phone use (in our
utility context) and other devices. These
matters are likely of interest to others, but do
not cover the essence of the gamification
project.
DELIVERABLES
1) We will have three working game
prototypes. We will have a dashboard
gateway that shows usage data, and has links
to the games and to an array of actions.
2) We will prepare a final report that details
the results of formal user testing of the
gamification system.
3) We will conduct a final review of relevant
literature, including newer, or newly
discovered, literature encountered since our
Phase 1 report.
4) We will submit research reports to the
professional literature on gamification and
electronic commerce (with funding credit to
Avista).
PRINCIPAL INVESTIGATOR(S)
Name Richard Reardon, Ph.D.
Organization University of Idaho, Dept. Psychology/Comm
Contact #208-292-2523
Email [e-mail]
Name Julie Beeston, Ph.D.
Organization University of Idaho, Dept. of Computer Science
Contact #208-292-2671
Email [e-mail]
RESEARCH TEAM
Name Mary McInnis, B.S (ME), M.S. (Hum Factors)
Organization Univ. of Idaho & Hum Factors Consultant
Email [e-mail]
Name Jode Keehr, M.S., Ph.D.(candidate)
Organization Univ. of Idaho, Psyc (Human Factors)
Email [e-mail]
Name Kellen Probert, M.S., Ph.D. (candidate)
Organization Univ. of Idaho, Psyc (Human Factors)
Email [e-mail]
Name UI Students (2-3 to be named)
Organization Univ. of Idaho, Dept. of Psychology
Email tba
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
1. Human User Privacy Clearances 3 months 9/20 12/20
2. Identify User Testing
Customer sample 3 months 12/20 3/21
3. Prototypes developed and tested 5 months 12/20 7/20
4. User testing of full
system 5 months 2/21 5/21
5. Research reports 4 months 10/20 2/21
APPENDIX H
Two-Page Report: Energy Storage & Real-time
Demand-Response
i
Evaluating the Effects of Energy Storage and Real-Time
Demand Response within an Enhanced Avista Energy
Trading Platform Prototype
Project Duration: 9 months, due 2021-05-31 Project Cost: Total funding $77,027
OBJECTIVE
In past years, we developed a prototype
system the Avista transactive power (ATP)
application that successfully integrates a
managed transactive energy market with
power flow analysis and distribution locational
marginal prices (DLMP). ATP enables the study
of approaches to create a transactive energy
market while ensuring a feasible and cost-
effective operation of the distribution grid that
does not violate operational limits. In this
project, we develop a smart building
simulation software prototype system and
integrate said prototype with ATP. This
enhanced toolset would enable us to analyze
demand-response scenarios and determine
how smart buildings could help save energy
while maintaining a secure and safe
operational power grid state. We are also
developing a set of power system scenarios for
testing and evaluation by adding distributed
energy resources to a distribution grid based
on the IEEE-34 bus system.
Avista and Idaho consumers would benefit
from the results of this research in the
following ways:
Deliver a prototype platform for testing
new technologies and algorithms to enable
large-scale evaluations of grid-secure
interactions between smart-buildings and
the utility.
Enable engineers to create accurate models
of the interaction between smart-buildings
and the electric distribution grid. This
should help the utility with managing the
grid in a more efficient, lower-cost manner
as the number of connected smart
buildings increases.
Enable smart building owners to model the
overall cost and potential cost savings of
different building management strategies.
Enabled by new building construction and
driven by the need for more energy-efficient
buildings and operational cost savings, smart
buildings' connection to the distribution grid is
accelerating.
Smart buildings have several and varied
capabilities that may enable a more efficient
operation. Smart buildings may also have the
capacity to help the grid in times of need by
changing their consumption behavior or even
injecting power into the grid if needed. It is
possible that if managed well, such an
interconnected system, called the smart grid,
may help utilities maintain the current quality
of service without heavy investments in new
distribution infrastructure.
The electric power grid's consequences of
adding large numbers of distributed energy
resources and smart-buildings to the power
grid are not well evaluated today and need to
be researched and investigated.
For the smart grid to be successful, its
implementation needs to keep or improve the
current high service levels and low energy
cost. Utilities need tools that would enable
them to model, study, analyze, and evaluate
the engineering and economic consequences of
connecting large numbers of distributed
energy resources (DERs) and smart buildings
to the distribution grid. This project aims to
solve one of those needs.
SCOPE:
Task 1: Review literature on smart
building and prosumer models and
communication protocols.
We evaluated and tested using OpenADR for
building to utility communications and found
that OpenADR is not well-suited for the type of
information exchange needed. We now began
to develop our own protocol implementation.
Task 2: Evaluate and document available
libraries and toolsets for power system
dynamic analysis.
Research has been conducted on available
libraries and toolsets for power system
dynamic analysis.
Task 3: Design and implement a rich
system model with renewables, storage,
and transaction intent-set
The model developed within the Phase I
section of the project has been successfully
enhanced from the IEEE 13 bus system to the
IEEE 34 Bus system. This IEEE 34 Bus system
has been modified to incorporate smart
buildings
Task 4: Design and implement
autonomous smart building and prosumer
agents and integrate the demand-
response agents with the market sub-
system
The design and implementation of a software
system to simulate smart buildings with
demand-response capabilities are currently in
progress.
Task 5: Perform steady-state, pricing, and
dynamic analysis under a few different
demand-response scenario variations
based on the scenario model from Task 3
Different scenarios will be designed to study
the impact of varying model power system
prices and operating points. This task is
currently ongoing.
Task 6: Integrate all sub-systems:
Agents, Market, Pricing, Sys. Model,
Power Flow, Dynamic Analysis.
The integration of all sub-systems will
commence once Tasks 4 and 5 are complete.
Task 7: Write a final report with details of
integrated prototype and experiment
analysis and results
This task will be completed once Task 8 has
been completed.
DELIVERABLES
The deliverables upon successful completion of
this project, including the software prototypes,
will be:
Written final report of the results of
these studies in the format approved by
Avista.
Interim reports and online conferences
with Avista. Mid-term report.
Proof-of-concept software toolset and
documentation.
Evaluation using an enhanced IEEE-34
bus model and results.
PROJECT TEAM
PRINCIPAL INVESTIGATOR
Name Dr. Yacine Chakhchoukh
Contact #(208)-885-1550
Email [e-mail]
CO-PRINCIPAL INVESTIGATOR
Name Dr. Daniel Conte De Leon
Contact #(208)-885-6520
Email [e-mail]
Name Dr. Herbert L. Hess
Contact #(208)-885-4341
Email [e-mail]
Name Dr. Brian Johnson
Contact #(208)-885-6902
Email [e-mail]
SCHEDULE
Task
Item
Start Date Finish
Date
%
Completion
Task 1 09/06/20 10/15/20 100%
Task 2 09/06/20 10/15/20 100%
Task 3 09/06/20 12/07/20 100%
Task 4 10/11/20 02/15/21 15%
Task 5 11/11/20 02/15/21 50%
Task 6 02/01/21 04/30/21 0%
Task 7 04/30/21 05/31/21 0%
APPENDIX I
Two-Page Report: Automating Predictive
Maintenance for Energy Efficiency

Automating Predictive Maintenance for
Energy Efficiency via Machine Learning and IoT Sensors
Project Duration:​ 12 months Project Cost: ​Total Funding $82,112

OBJECTIVE 
Our goal is to develop an energy
management decision support tool aimed at
helping small-to-medium size businesses.The
purpose of the tool is to leverage sensors
attached to mechanical systems to automate
prediction and optimization of energy
efficiency and reduce operational costs.We
plan to accomplish this using a commodity
Internet of Things (IoT)platform and machine
learning to automate the prediction and
optimization procedures.
BUSINESS VALUE 
The keys to saving energy include the
implementation of energy management
techniques,specifically equipment
maintenance and monitoring techniques​1​.In
addition,predictive maintenance uses
equipment sensors (manually or automatically
operated)that indicate and predict when
maintenance will be required.
INDUSTRY NEED 
Large businesses and corporations benefit
from the use of virtual energy assessment
and energy modeling provided by
commercially available third party tools​2​.For
the remainder of the business sector,current
energy consumption,usage,and loss
assessment are labor intensive,lack
automation,lack an incorporated learning
mechanism,and usually depend on costly
sensors.Yet,when these same companies
follow general strategies for preventative and
predictive maintenance,they can improve
1Bucklund S., Thollander P., Palm J. and Ottosson M.,
“Extending the energy efficiency gap,” Energy Policy 51,
pp 392--96, 2012.
2https://www.inversenergy.com/, accessed April 22, 2020.
energy efficiency by up to 30%​3​.Using the
system we develop,small to medium sized
businesses will be enabled to automatically
monitor the energy efficiency and
maintenance needs of mechanical equipment.
Connecting their systems to our online,
data-driven,decision-support tool,business
owners can make more informed decisions to
optimize energy efficiency and reduce costs.
BACKGROUND 
Both sensors and a commodity IoT platform
that can serve as the basis for these sensors
are readily available.Additionally,machine
learning has been shown to be highly
effective at predictive modeling​4​.Combined,
these are capable of automatically collecting,
propagating,and assessing underlying
maintenance data,all of which are necessary
to develop the tools required by managers to
effectively plan and manage energy efficient
maintenance​5​.
SCOPE
Task 1: Identification and procurement of
equipment items to monitor and lab setup We identified motors, pumps, etc., that could
be monitored for predictive maintenance. We
have identified and procured: 2 dryers (w/
motors), 1 blender (w/ motor), 1 water
pump, and 1 free-standing motor. We also
procured sensors, Raspberry Pis, a server,
3Firdaus N. et al, “Maintenance for Energy Efficiency: A
Review,” Proceedings of the IOP Conference Series:
Materials Science and Engineering, 2019.
4Mosavi A., Bahamani A., “Energy consumption
prediction using machine learning; a review,”
5Lewis A., Elmualim A. and Riley D., “Linking energy
and maintenance management for sustainability through
three American case studies.” Facilities. 29 Issue: 5/6, pp.
243--254, 2011.
and internet connectivity for system
development.

Task 2: Development of a cost effective, general
IoT-based sensor platform for automated
collection of operational data for predictive
maintenance We have built an IoT-based sensor platform
consisting of a Raspberry Pi connected to 6
mechanical sensors, each measuring a
different aspect of the monitored equipment.
Software has been implemented to read and
transfer sensor data to the data server.

Task 3: Development of an online, data-driven,
decision-support tool for improved energy
efficiency We have completed development of a server
portal housed on a data server hosted at
ISU’s Research Data Center. Once completed,
the server receives and aggregates data from
all connected IoT-based sensor platforms. The
aggregated data will then be automatically
and regularly analyzed using machine
learning algorithms to predict energy
efficiency and maintenance needs for the
equipment associated with each sensor
platform.

Task 4: Development of a mobile-friendly web
data dashboard We will implement a dashboard to allow users
to monitor performance of mechanical
systems.The dashboard will show both data
collected as well as predicted efficiency and
maintenance needs in a user-friendly format
that can be accessed via web interface on
mobile or desktop devices.

Task 5: Training and testing of completed IoT
and predictive maintenance platform We plan to train an instance of the online,
data-driven, decision-support system (task 3)
using data collected from mechanical systems
(task 1) via the implemented IoT sensor
platform (task 2) in order to test the
functionality of the developed systems.
Experiments will be conducted to simulate
failed mechanical systems so that the system
is able to generalize from data patterns
stemming both from operational and
underperforming machines.

Task 6: Training of students in the development
of smart energy efficiency tools, providing
hands-on industrial experience and reinforcing
classroom learning.
We recruited 2 mechanical engineering and 1
computer science undergraduate senior
students who, under the guidance and
supervision of faculty researchers, have
developed the software and hardware
solutions necessary for the predictive
maintenance system. In doing so they have
developed niche expertise, working in a team
setting, in the domain of predictive
maintenance technology.
DELIVERABLES 
1.Software representing a cost effective,
general IoT-based sensor platform for
automated collection of operational data
for predictive maintenance
2.Software representing an online,
data-driven,decision-support tool for
improved energy efficiency in maintenance
practices at small-to-medium businesses
3.Software representing a web dashboard for
data collection and analytics for monitored
systems
4.Experimental results demonstrating the
effectiveness of the combined system at
predicting energy efficiency and
maintenance needs
PROJECT TEAM  
SCHEDULE 

PRINCIPAL INVESTIGATOR(S) 
Name Paul Bodily, Isaac Griffith
Organization Computer Science Dept, Idaho State University
Contact # 208-282-4932 (Paul)
Email [e-mail]​, [e-mail]
Name Marco Schoen, Mary Hofle, Anish Sebastian,
Kelly Wilson, Omid Heidari
Organization Mech Engineering Dept, Idaho State University
Contact # 208 282-4377 (Marco)
Email
[e-mail]​, ​[e-mail]​,
[e-mail]​, ​[e-mail]​,
[e-mail]
RESEARCH ASSISTANTS 
Name Andrew Christiansen
Organization Computer Science Dept, Idaho State University
Email [e-mail]
Name Avery Conlin, Safal Lama
Organization Mech Engineering Dept, Idaho State University
Email [e-mail]​, ​[e-mail]
TASK TIME ALLOCATED START 
DATE 
FINISH 
DATE 
Mech Sys Procurement 2 months Oct 2020 Nov 2020
IoT Sensor Platform Dev 4 months Nov 2020 Feb 2021
Online decision-sup Dev 6 months Nov 2020 Apr 2021
Data Dashboard 4 months Mar 2021 Jun 2021
System training/testing 4 months Apr 2021 Aug 2021
Training students 10 months Oct 2020 Aug 2021