20170215Blattner Exhibit 39.pdf

Ronald L. Williams,ISB No. 3034
Williams Bradbury, P.C.
1015 W. Hays St.
Boise,ID 83702
Telephone: [phone]
Email: [e-mail]
Attorneys for Intermountain Gas Company
BEFORE TIIE IDAHO PUBLIC UTILITES COMMISSION
IN THE MATTER OF THE APPLICATION OF
INTERMOI.INTAIN GAS COMPANY FOR
THE AUTHORITY TO CHANGE ITS RATES
AND CTIARGES FOR NATURAL GAS
SERVICE TO NATURAL GAS CUSTOMERS
IN THE STATE OF IDAHO
CaseNo. INT-G-16-02
BXHIBIT 39
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Case No. U- 1034- 134
INTERMOUNTAIN GAS COMPANY
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BEFORE THE
IDAHO PUBLIC UTILITIES COMMISSION
ln the Matter of Application of
lntermountain Gas Company For Approval
of its Weather Normalization Model
REVISED TESTIMONY AND EXHIBIT
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 1 of 8'l
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IMERI,IOTINTAIN GAS COMPANY
CASE No. U-1034-134
REVISED
Subsequent to the original fl1ing of U-1034-L34, lnconststencleswere discovered ln some of the blIling data for the perlod
October, L979 through June, 1980. Therefore, all data durlngthls perlod was discarded l-n the revlsed study.
The data used ln the model was updated to include December 1985,
through June, 1986. This captures the most recent available dataand al-lows the study period to contaln an adequate nuuber ofhlstorl-cal data points la relatlon to the nunber of predictlngvarlables. 'The models were then refitted to that data.
The statistically non-slgnlficant data included in the orlginal
f lll.ng, has been oml-tted from the revlsed f lllng. Anyone
interested ln that data can obtaln it from the origlnal flling or
by reguest to Intermountain Gas Company.
Exhibit No. 39
Case No. INT-G-16-02
L. Blaftner Reb.
Page 2 of 81
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Case No. U-1034-134
INTERMOUNTAIN GAS COMPANY
(Request for Approval of Weather Normalization Model)
REVISED PREPARED DIRECT TESTIMONY
OF
JOHN M. KOHLMEIER
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Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 3 of 81
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Would you please state your name and business
address.
My name is John M. Kohlneier and my business
address is 33 West Monroe Street, Chicago,
Illlnois 60603.
By whom are you employed and in what capacity?
I am a partner with Arthur Andersen & Co. f am
responsible for directing our planning, fore-
casting and modelj-ng consulting practice for
clients in the utllity industry in the United
States.
Would you please outline your academic and
professional background?
I was awarded a B.A in Economics with honors
from Dlckinson College in 1955r dtr MBA from the
Harvard Graduate School of Business Administra-
tion in 1960 and a DBA from Harvard in L964,
From 1963 through 1968 I was an Assistant
Professor in the Graduate School of Business at
the University of Chicago where f taught and did
research in accounting and quantitative methods
specializLng in modeling. f have been wlth
Arthur Andersen & Co. since 1968 specializing in
planning, forecasting and modeling consulting
work for clients in the electricr gds and
Kohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 4 of 81
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telephone utility industries. I have been a
member of several AICPA task forces that have
developed st,andards for financial- forecasting.
What is the purpose of your testimony in this
proceeding?
The purpose of my testimony is to describe the
methodology developed by the Conpany to adjust
gas sales for weather normalizatlon and declin-
ing use to be used in the presentation of test
year data ln future rate cases.
Why is it appropriate to adjust gas sales for
weather normalization and declining use?
Gas sales for certain classes of customers are
highly sensitive to the temperatures actually
experienced during the test, year. Normali-zing
for weather reduces the rate instability that
could occur if rates were based on actual sales
in a test period in which actual weather dif-
fered from normal by a significant amount. fhe
use of normal temperatures ior ratemaking is
generally regarded as the best estimate of the
future weather and resulting sales that will
actually occur.
Gas consumption per customer has experienced a
long-term declining trend due to the use of more
Kohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
Case No. INTIG-16-02
L. Blattner Reb.
Page 5 of 81
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energy-efficient technology, the replacement of
o1der, less efficient appliances with new, more
gas efflcient appliances, conservation and other
enerqy saving measures. Failure to provide for
declining use introduces a systernatic bias
whereby the Company will never be able to earn
its authorized rate of return. By not allowing
a declining use adjustment, sales and revenues
are conslstently overestimated resulting in
consistently overestimated returns that will
never actually be achieved.
Declining use and weather normalization adjust-
ments represent known and measurable changes to
historical test year results.
What evidence is there that gas sales for
Intermountain Gas Company are weather sensitive
and experience declining usage?
For the Companyrs residential and small
commercial customer classes weather represents a
very significant portion of the month to month
variation in gas sales for the period october
1979 through June 1986.
During the same period per customer usage
declined in a persistent, statistically signifl-
cant, long-term trend. This decline in usage
Kohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
Case No. tNt:G-to-02
L. Blattner Reb.
Page 6 of 81
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has persisted through June 1986, the most recent
month in the study data.
What are the principtes underlying the Companyts
approach and methodology to weather normaliza-
tion and declining use adjustments?
The methodology should be based on variables and
relationships which make both logical business
sense and are statist,lcally va1id. Mode1s whlch
are statistically valid but which represent
illogical or nonsensical economic relationships
are not acceptable. In addition the methodology
should provide procedures for updating and
refitting the models in the future lf conditions
change.
P1ease outline the Companyts new weather and
declining use adjustment methodology.
The Companyrs methodology consists of the
following procedures:
L. Relationships and models are hypothesized
to explain gas saIes.
2. Monthly data is collected for use ln the
analysis.
3. Regression analysis is used to quantify
and test the relationshlps between sales
and the explanatory variables on a per
Kohlmeier, Revised Di
Intermountain Gas company
Exhibit No. 39
Case No. trurjc-to-oz
L. Blattner Reb.
Page 7 of 81
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customer basis.
4. The resulting models are used to adJust
actual sales for weather and to estimate
declinin€t use.
What variables and rnodels were hypothesized to
explain the variation of therm usage per
customer?
It was hypothesized that the following types of
variables would explain therm sales per
customer.
1. A constant term.
2. Weather in the form of degree days.
3. Long-term usage trend.
4, Price of gas and electricity.
5. Demographic and economic variables such
as GNP, income, employment/unemployment.
5. Appliance saturation.
7. Seasonal variables reflecting different
month by month usage.
Please describe the data used.
Monthly data was used because reliable data for
days or weeks is not available for therm sales
per customer. In addit,ion, insufficient time
periods of data were available to use quarterly
or annual therm sales and still pass the statis-
Kohlmeier, Revised Di
fntermountain Gas company
Exhibit No. 39
case No. tNrjc-to-oz
L. Blattner Reb.
Page 8 of 81
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tical requirements.
Therm sales data and related weather data was
collected by customer rate c1ass, RS-I
(residential space heating), Rs-2 (residential
space and water heat,ing), cS-10 (non-cyc1ical
Gs-1), GS-11 (cyclical Gs-1), and GS-20 (other
Gs-I) for the fLve divisions, for a total of 25
rate class/division categories for each month.
We also constructed weighted average data to be
able to properly combine dlvisj.ons and the three
individuaL Gs classes and compare the combined
results with the individual class/division
results.
Monthly thern sales data was taken from the
Companyrs historical billing register for the
period from october 1979 through June 1986,
which provided an adequate base for the statist-
ical analysis. Data prior to October L979 ls
not aval-Iab1e ln appropriate format. Due to
some inconsistencies in billing data, the months
of October L979 through June 1980 were dis-
carded; the final models use data from July 1980
through June 1986. Billing data represents
monthly time periods but is based on billing
cycles and does not precisely represent therm
Kohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
case No. ttttlc-to-02
L. Blattner Reb.
Page 9 of 8'l
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consunption durlng that calendar month. As a
result, the degree day temperature data was
adjusted cycle by cycle to correspond on a
weighted average basis with the degree days
actually included in the billing data. Data was
collected from external sources for electrical
price, unemployment, personal income, GNP and
appliance saturation.
What were the results of the regression
analysis?
In general the regression models developed fit
the data extremely weII. The R squares for each
model were at least 98.92. It was found that
aggregate Company-wide rnodels fit better than
individual divisional models. It was also found
that combining the three srnalI commercial rate
classes into one group produced signlficantly
better results than the individual snall- com-
mercial rate classes taken individually.
None of the econonic/dernographic variables
proved to be statistically significant and these
$rere eliminated from the models. Appliance
saturation was also discarded foq the same
reason. AIl gas and electricity price measures
were also found to be insignificant and were not
Kohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
Case No. trurlc-to-02
L. Blattner Reb.
Page 10 of 8'l
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used in the models.
The best, fit resulted when two degree day
variables were included. The first represented
degree days calculated from t-he traditional 650
base. The second series was calculated from a
45o base. The rationale for this is that
customers behave differently with respect to
very cold temperatures than they do to moderate
temperatures.
A long-term customer usage trend variable which
had the property of increasing by one each
heating season month (November through April)
and being zero in non-heating season months fit
the models well and was significant. A simple
time trend variable which increased by one each
month did not fit as weII.
Monthly seasonal binary variables for the months
of october through April proved significant and
worked better than variables for the alternative
summer months (May through September).
Please describe the models produced by the use
of this methodology.
The rnethodology described above applied to the
data for the six-year period JuIy 1980 to
June 1986 produced three models (Rs-I, RS-2 and
Kohlmeier, Revised Di
fntermountain Gas company
Exhibit No. 39
Case No. tur:c-'to-oz
L. Blattner Reb.
Page 11 of81
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GS) of almost identical form. These models are
very logical from a business or economic theory
standpoint and have very strong statistical
support. The model Ln each case predicts actual
therm sales per customer in the class for the
month based on variables which are not highly
correlated, which are all statistically signifi-
cant at the 952 leve1 and together produce
remarkably high R square. The models are
presented in the following tabIe.
FINAL REGRESSION I',IODELS BY RATE CLASS
RS-]. RS-2 GS.lcoefficient Coefficient Coefficient
fntercept 5.509 23.584 L48.73
HDD45 0.0207 0. 0152 0.3045
HDD65 0.0678 0,0799 0.2573
october (3.981) (6.928) 68.84
November 5.255 222.27
December L4.995 9.44L 282.4L
January 19.333 1-4,479 279.47
February L9.237 12.736 259.02
March L4.542 9.52L 225.93
April L0.598 7 .4l-9 183.50
Usage Trend (0.130) (0.078) (4.534)
,Adjusted R- 99,7 99 ,7 98.9
A more detailed presentation of the models and
the nethodology is contained in Exhibit I.
Kohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 12 of81
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How will the results of th models be used to
foradjust actual therm sales for the test year
weather normalj.zation and declining use?
IIt is anticipated that in tfre preparation of a
test year, the models wiff ' Ue refitted. to the
Idata then available and I the parameters re-
estimated. i
The rnodels will then be rurr for each month of
Ithe test year, once wltfr normal weather
(temperatures) and once witlt the actual weather
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(temperatures) experienced ;during each of the
imonths of the historical test year. The
difference ln predicted per icustomer therrn usage
Iwill then be multiplied by the actual number of
customers and the total wil,l be added or sub-
tracted. from the actual til"rm sales for each
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month for each of the three ]customer classes to
produce normalized therm satres.
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The declining use adjustmdnt to test period
Itherm sales will be calculated by multiplying
!the usage trend coefficierit by the relevant
increment for each winder heating month
(Novernber through April) and then multiplying
the number of customers in the class for that
Kohlmeier, Revised Di
fntermountain Gas Company
Exhibit No. 39
Case No. tHr-G-to-oz
L. Blattner Reb.
Page 13 of 81
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month in the test period and summing the monthly
Iamounts to a total for the year. The resultant
itotal therms will be subtracted from the weather
adjusted therm sales for the test period.
How does this metnoaorogyi and the resultant
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models compare with the rnethodology formerly
iused by the Company particularly with respect to
the crlticisms of the previous methodology made
by the Commission Staff. I
The Company has adopted tll.e best methodology
Iavailable and the' resulti4g models make both
Igood business sense and havd strong statistical
!support. i
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The methodology and resulteint nodels presented
above correct the criticisms raised by Messrs.
Ferguson and Santos of thelcommission Staff in
Ithe two most recent general lrate cases. In fact
ithe new methodology used by,the eompany is very
simllar to that used by tfle conmission Staff.
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The Company has improved tHe quality and pre-
Icj-sion of the data. It has also tested many
Ialternative formulations arid thereby improved
the fit of the models.
The Staff criticized the fact that the prior
methodology utilized the sales in a single month
Kohlmeier, Revised Di
fntermountain Gas ComPanY
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 14 of81
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does not produce a base load per se, it fu1ly
reflects data for the entii'e stud,y period used
to develop the models. Addltionally, variables
reflecting both seasonal variiation and long terrn
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usage trend are present in tne models.
The Staff crit,icized the fact that formal
Istatistical procedures werd not used and that
the statistical validity of the results could
to establish base load and
not vary over time. While
not be verified. The
utilizes fornal statist
models produced have
tistical support.
Companyrs nethodology did
evaluating the effect of
Page 12
that base load did
the new methodology
methodology fu1Iy
procedures and the
gh degree of sta-
not provide for
other variables on
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Mr. Ferguson criticized the fact that the
:therm consumptj,on. The Companyts new nethod-
ology provides a way to
different variables.
analyze and test
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iKohlmeier, Revised Di
Intermountain Gas Company
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 15 of81
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Case No. U-1034-134
INTERMOUNTAIN GAS COMPANY
(Request for Approval of Weather Normalization Model)
REVISED PREPARED EXHIBIT
OF
JOHN M. KOHLMEIER
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 16 of 8'l
t
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IntermountaLn Gas Company
Case No. U-1034-134
Revised Exhibic No. 1
Page I of 65
Kohlmeler
INTERMOI'NTAIN GAS COMPANY
WEATIIER NORMALIZATION AND DECLINING USE METHODOLOGY
Table of Contents
I. Methodology Overvlew.
II. Detalled Methodology Descriptlon.
A. l,latchlng Weather Data wlth Sales Data.
i)ii)
i11)
lv)
v)vl)
General Discussion
Weather Data Sources
8111-tng Cycle Dall-y I{eighEs
Constructlon of WeaEher Actuals
Constructlon of !,Ieather Nor:mals
Sales Data
B. Descrlptlon of Economlc and Prlce Data.
C. Descriptlon of Regresslons.
i) General Dlscusslons
11) Statistlcal Resulrs
D. The Use of Regression Results to AdJust for Normal tleather.
E. Decllnlng Use Adjustment.
III. Data
A
B
c
Data l,Inemonics.
Baslc Data.
ModeLs and Predicted Results.
Exhibit No. 39
Case No. INI-G-16-02
L. Blattner Reb.
Page 17 of 8'l
Intermountain Gas Company
Case No. U-1034-134
Revlsed ExhLblt No. 1
Page 2 of 65
KohLmeler
WEATIIER NORI'IATIZATION AI{D DECLINING USE METHODOLOGY
I. Methodology Overvlew.
Io determlne the degree to which actual therms sales were hlgher or
Lower than normal as a result of actual weather' iL ls necessary to
flrst quantlfy the relationship betrseen weather and sales. Thlsquantiflcation is achleved through the use of multlple regressLon
analysls at Intermountain Gas Company (IGC). Therm usage ls
statlstical-ly estimated as function of weather and non-rseather
varlabLes. The relatlonshlp between gas use and lts naLn
determlnants ls measured in three regresslon equatlons, one whlch
explalns residentlal space heatlng-only salesr orr€ whlch explalns
residential space and water heatLng sales, and one whlch explains
small commerclal sales. A1l- eguatlons are systern wide.
To explain gas use, the regresslon equations use weather concepts,
such as heating degree days, and economlc informatlon, such as gas
prlce, Comblnlng data for aLl months of at l-east slx flscal years in
each regresslon allows enough variation within the data to gain
lnformatlon from more than one weather concept and from economlc
varlables. The summary statistics for the equations demonstrate that
the regresslons are accurate {n explalning Eonthly varlations ln
saLes as ls detalled ln the methodology descriptlons. Each
regresslon r.ras tested for autocorrelation and correcEed when
necessary,
Exhibit No. 39
case No. INT:G-16-02
L. Blattner Reb.
Page 18 of 81
Intermountaln Gas Company
Case No. U-1034-134
Revised Exhibit No. I
Page 3 of 65
Kohlneier
WEATHER NORMALIZATION AND DECLINING USE METHODOLOGY
Once the regresslon equatlons have been specifled and estimated, ltls the coefficLents of the weather varlables that are lmportant tothe weather adJustnent process. These coefflclents measure the
response of sales to changes in Ehose weather variables. For
example, the coefflcient of the heating degree day (base 65')
variable ln the space heatlng only, resldentiaL total system equation
represents the number of additlonal therms per customer Ehat oneaddltional- heating degree day (base 65") wouLd cause. Thecoefflcient of the heating degree day (base 45o) varlable representsthe number of addltional therms per customer that one additlonal
heating degree day (base 45o) would cause. Two weaEher varlables are
used because an additlonal degree drop in t,emperature below 45"
causea more additional use of gas than a degree drop in tenperature
between 65o and 45o. The effect of the HDD45 ts additive to theeffect of the HDD65'. By muLtlplylng these coefflcients by the
dLfference between Ehe normal and acEuaL heatlng degree days (base
65') and heatlng degree days (base 45o), the dlfference beEween
aetual and normal therms per cust,omer ls determined.
Note that the non-weather variables are used onLy to estimate the
regression equatLons and that only the coefflcients of the weaEher
v"iiabl"s are used in the acriEfadJustment. Al-so note that Ehe
primary purpose of Ehe models is to adJust sales Ehat have already
occurred rather than to predict future sal-es.
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 19 of 81
Intermountaln Gas Company
Case No. U-1034-134
Revl-sed Exhiblc No. I
Page 4 of 65
Kohlnel-er
WEATHER NORMALIZATION AND DECLINING USE I"IETI{ODOLOGY
II. Detalled Methodology Descrlptlon.
A. Matching Weather Data Wlth Sales Data
1) General Dlscusslon
The weather concepts in the regressions use Eemperature data fromflve weather statlons: Caldwel1, Bolse, Idaho Falls, Pocatello, and
Twln FalLs, Idaho. One of the critical aspects ln quantlfying the
relatlonshlp between gas use and weaEher is the correct matching of
weather data wlth sales data. Because resldential and some
conmerclal customer are bIlled ln cycles, sales in a partlcular month
represent sales in the current month and previous month, dependlng
upon the bllIing cycle belng anal-yzed. To correcEly match rueather
and sales, the weather varlables used ln the analysis represenE
weighted total-s of the dally values of these variables over the
appropriate tlme periods. Each dayrs weather measure is weighted by
Ehe number of customers (for a particular tronth) who expertenced EhaE
weather.
The IGC service area eontains dlvlsions rvhlch experience differenE
temperatures at a given point ln tine. To ineorporate these
dlfferences ln the system regressions, the weather variables were
constructed ustng weather data weight.ed by the number of customers ln
each of the divisions represented by the Caldwell, Bolse, Idaho
Fall-s, Pocatello and Twln Falls weather statlons. By construct,ing
the weather varlables in this r.{ayr the regression has the capabillty
of lncorporatlng the dlversity of weather in the Companyrs service
area. Since the aggregate company wlde nodeLs flt the data better
than the lndlvldual dlvislonal models, the aggregate conpany wlde
models have been used.
Exhibit No. 39
Case No. INT-G-'|6-02
L. Blattner Reb.
Page 20 of 81
I
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Intermountain Gas Company
Case No. U-1034-134
Revlsed Exhiblt No. I
Page 5 of 65
Kohlmeier
WEATHER NOR}IALIZATION A}ID DECLINING USE METHODOLOGY
II. A. 11) Weather Data Sources
the sources of all- data ls the National Oceanic and
Atmospheric Adnlnistration (nOel;. Dally maximum and
mlnlmurn temperatures for October 1979 f or:r^rard are used to
cal-culate actual heatlng degree days (UOn; for base 65o and
base 45o, The N0AA al-so provides a thlrty year normal
heatlng degree day base 65o of dally data for a one year
"normaltr period. This normal Ls calculated fron the years
1951-1980. The normal HDD base 45o are calculated fron the
base 65" on a dally basls.
Exhibit No. 39
Case No. INT:G-I6-02
L. Blattner Reb.
Page 21 of81
Intermountain Gas Company
Case No. U-1034-134
Revlsed Exhibit No. 1
Page 6 of 65
Kohlmel-er
WEATHER NOR},IALIZATION AND DECLINING USE METHODOLOGY
II. A. 111) BlLllng Cycle Daily I'Ieights
The weather concepts used ln the weather adjustuent analysls are
matched with sales ae follows:
1) Monthly weather varlabl-es are eai-culated by aggregatlngdally data over the approprlate b1111ng cycle and thentaklng a welghted average of these totals based on the
number of servlces in each bllllng cycLe, giving b1L1ing
month totals.
2) The nonthly totals are then comblned inEo system wlde(servtce area) aggregate weather measures using dlvislonal
service welghrs.
D. Cyele welghtlng of weather for b11-11ng cycles.
a)
I{DD45 day
l-ast (cycle month)
cycLe month = d, = fr-rst (cycJ-e monrh)
I{DD45
I{DD55 cycle month =
last (cycle month)tdiy = first (cycle month)
HDD65 day
Then:
b)services
IIDDA5 HDD45
,cycles
4flcles
month
cycLe nonth
alJ- cycles in month
cycle month
serv{ces
services
HDD65 cycle month
all cycles in monEh
cycle monthHDD65monEh
services
Exhibit No. 39
Case No. INT.G-16-02
L. Blattner Reb.
Page 22 of 81
I
II. A
Intermountalh Gas Courpany
case No. u-1b34-134
Revl-sed Exhlbtt No. I
Page 7 of 651Kohlmeler i
WEATHER NORMALTZATTON AND DECLINING USE I'{ETHODOLOGY
I
;
111) Bllling CycJ.e Dal1y Weights (continueh)
2) ComblnLng across divlslon 1-5.
Thern
RSO1AIL t services dlvlsloh
5
5
div=1 servLees all dlvlslons
(Therms
Divlslons)
(HDD4s
Dlvlslons)
(HDD6s
DJ.vlslons )
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 23 of 81
HDD45
HDD65
servlces
RS02AL1 t dlvislop
div=1 servlces Idlvls
I
I
!
I
a1L l"ong
5tdlv=1 services taII clLv:l-slons
servlces divisloirGSALL
Intermountalh Gas Coupany
case No. u-{034-134
Revised Exhlblt No. I
Page 8 of 651Kohlmeler i
:
WEATIIER NORMALIZATION AND DECLINING USE IIETHODOLOGY
II. A. lv) Coostructlon of Weather Actuals
mD65 Idally
where average dally temperature =l
max temP + min temp I
dalLy
2
= max [ 0, t{DD65 - 20]
nax [ 0, 65 - average dalil.y temperatureJ
r{DD45
Exhibit No. 39
Case No. INT-G-1&02
L. Blattner Reb.
Page 24 of 8't
I
Intermountaih Gas Company
case No. u-q034-134
Revised Exhlblt No. I
Page 9 of 651Kohlrueler
i
WEATHER NORMALIZATIO}I AND DECLINING USE METHODOLOGY
II. A. v) Constructlon of Weather Normals
r{DD65 dally max [ 0, 65 - average dalJ-y tenperature]
where average daily temperature =
max temp + nln temp
HDD45 da1I-y
2
= max [ 0, HDD65 - 20]
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 25 of 8'l
Intermountalh Gas Company
case No. u-1b34-134
Revlsed Exhiblt No. 1
Page 10 of 65Kohlneier i
WEATIIER NORMALIZATION AND DECLINING USN IUMODOLOGY
II. A. vt) Sales Data
Sales Data rsas taken from the Compartyts historLcal btllingregLster. Data fron therm sales, nunber of active services
and therm bi1L1ng adJustnents were avallable on a bllIlng
cycLe basis from October 1979 through June 1986. llowever,
data frorn October L979 through Junei 1980 was found Eo be
inconslstent and therefore, rras not 'used in the regresslon
analysis. The Conpany has 20 btlling cycles plus nonthly
non-cyclic servlce. For cycJ"ie service, therms weretotalled for each calendar blLling I month and adJustnentsapplled to the previous monthts iotal. For non-cycle
service, adJustnents trere appl-led for the prevlous monthfs
number of total therms. I
Average thenn use per service for eacfr nonth rtas computed to
be these adJusted therms divlded by t[e number of servlces.
Service counts on blIling cycle basis, were used to calcuLate
correctly welghted monthly neather treasures and to comblne
across rate classes and dlvlsions. i
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 26 of 81
Intermountalh Gas Company
Case No. U-1p34-134
Revlsed Exhiblt No. 1
Page 11 of 6bKohlmeler i
I,IEATTIER NORMALIZATION AND DECLINING USE i,IETHODOLOGY
II. B. DESCRIPTION OF ECONOMIC AI{D PRICE DATA
Prlce Terms - Each prlce term ls an average price, cal-culated as
revenues divided by therms. In all nodels, Ehe prlce tern is
lagged by two months to reflect that bill1s for the previous
,onths use do not arrive until the niddl-e pf the following month.
Therefore, consumers cannot react to ehangles untll a month and a
half has past. Therms and revenues used ln the calculation are
normaLized, as approved ln the last Rate Case (General or
Tracker) and are used on a nominal basls.
Trend In Gas Use CharacterLstlcs:The usage Erend term lncreases
by one ln each heating load month (November-Aprtl), but is zero
Ln all summer months. This variable refl-ects heatlng-related
applicatlon of more efficlent technology, the replacement of
older less efflcient appllances rsith new more gas efficlent
appl-iances, and conservatlon effects over the study perlod.
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 27 ol 81
Intermountaln Gas Conpany
case No. u-fo:+-t:t
Revlsed Exhlbit No. I
Page 12 of
Kohlmeler
5
Plus
pi-us
,trtrtpttea by
*rirrrptt"a uy
ofus
mttrtplted by
6
i
1
I
WEATHER NORMALIZATION AND DECLINING USE !I'IETHODOLOGY
II. C. Descrlption of Regressions
1) General Dlscusston
The regression equations relate therm ruse per service to afunction of actual weather (neasured as iIlDD45o base and I{DD65"
base), monthly binary varLabl-es for October-Aprll, and a usage
trend varlabl-e. The nonthly binary variables al-low base Load to
vary over the months of the year, whlle the usage trend varlable
allows base load to vary over time. The actual rveather variables
al-low measurement of the effect. of weather on usage.
I
The base load ln any month of the study pbriod can be calculatedas Ehe equatlon intercept plus the coefficient of the monthly
blnary variabl-e lf ve are calcuLatLng base load ln thaE month,
pl-us the coefficient of the usage trend rvarLable multiplied by
the vaLue of the usage trend variabLe ln t;har month.
IThe remainder of varlation ln gas sales iper service is weather
sensitlve, and ls rneasured by the coeffielgnts of the two weaEher
varlables urultipJ-led by the value of thosei variables.
I
For example calculate the base load in January of 1985 for
residential heatLng load customers (RS-l).,
Usage Trend CoefficLent (0.1298)
Usage Trend value 33
EQUALS 20.659 therms per servlce
t5.609 + 19.337 + (-0.1298 x 33)l = 20.659
Now calcul-ate weather sensltLve l-oad for the same month.
Intercept
Jan CoefficlenE
IIDD45 Coefficlent
I{DD45 val-ue
HDD65 Coefficlent
HDD65 value
EQUALS
[ (0.020669 x 731.818)
106.153 therms per service+ (0.067839 x 1341.81)l = 106.153
5. 609
19.333
0.020669
731 . 818
0.067839
1341.81
The total of base and weather sensitlve load equals acEual thern
usage less a small error Eerm. Other monthst usage per servtce
can be calculated in the same manner.
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 28 of 81
Intermountailn Gas Company
case No. u-{034-134
Revised Exh{bit No. I
Page 13 ot 615Kohlneler
I
WEATHER NORMALIZATION AND DECLINING USE I"IETHODOLOGY
A11 the explanatory varlables are starl{tlcal1y slgnlflcant at
the 95'l confidence Level. The RS-l customer class (resldential
heatlng-only l-oad) equatlon explains $9.7 percent of thevarlation Ln therm sales per service. The RS-2 customer class(residentiaL heatlng and hot water load) ; equatlon expl-ains 99.7
percenE of the variatlon ln thern sales gler customer. The GS-I
customer cLass (snalI eommerclal cust,omerA frorn GS-10, GS-l1 and
GS-20 equatlon explalns 98.9 percent of the varlation ln therm
sales per service.
IThe Durbin-Watson stattstlcs for the equatlons, rvhlch ueasure the
autocorreLatlon of the error terms (reslduals), show
autocorrelation of the residuals. Thls means that there ls a
pattern of variaEion in therms saLes whlch ls unexplained by the
regresslon equatlon. These correlaElons afe at l-ow orders, which
says that lf an error was negative ln month n, Ehere wouLd llkelybe a negatlve error in nonth n+1. Thisi auto correlatlon was
corrected using a first order autoregre,sslve equaEion on Ehe
resLduals which ls additlve Eo the nultiple regresslon equatlon.
It should be noted thaE the autocorreladlon does not blas Ehe
coefficlents of the explanatory variabled, and does noE assure
mlnimum error for the model. The autorqgressive procedure, ln
conJunction wlth the regression equation, ipproaches thls minimum
error.
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 29 of 8'l
Intermountain Gas Courpany
case No. u-{os+-ta+
Revlsed Exhlblt No. I
Page 14 of d5
Kohlneler i
I.IEATIIER NORMALIZATION AND DECLINING USE METHODOLOGY
II. C. lf) Statlstical- ResuLts
Final Regression By Rate CJ-as
I
s
RS-1Coef-T-Stat RS-2coef-T-stat GS-1coef-T-stat
Intercept
IIDD45
HDD65
Oct.
Nov.
Dec.
Jan
Feb
Mar.
APr.
Usage Trend
MSEadj. R2
AdJ. MSE(for comparison)
Durbln-I,Iatson
64)
7
3
9
7
1
4
2s)
9.441 514.479 6
12.7 36 69,52L 67.4t9 5(0.078) (
.71 ,
.80 i
.36 i
2.01)
2r.50
5,67
7 .87
5. 18
11.17
11 .66
9.58
10 .40
705
98.9
L0,92
.96
Exhibit No. 39
Case No. INT€-16-02
L. Blattner Reb.
Page 30 of 81
5.609
0.0207
0.0678
(3. 981)
5,255
L4.995
r9. 333
L9.237
14,542
10.598
(0.130)
9 .89
4.62
24.89
23.s84
0.0152
.0799
(6.e28)
38.0
3.47
34,9
(6:o
148. 731
0.3045
0.2573
68. 84
222.27
282.4t
279.47
259.02
22s.93
lB3. 60
(4.s34)
.40
,26
I
(i,
6
7t)
I
I
;
(3.
3.1
7.5
7.9
9.3
8,2
5.9(r.
10.63
8,62
(e.43)
5
99,7
46
99,7
2,96
r,57
2.99
1.51
I
Intermountai]n Gas Company
case No. u-{034-134
Revised Exhlblt No. I
Page 15 of 615
Kohlrneier i
WEATIIER NORMALIZATION AND DECLINING USE I"IETHODOLOGY
II. D. The Use of Regresslon Results to Adjust for Abnormal l,Ieather.
The method by which the preceding regressfon results are used
weather normalize nonthly sales ls the same method used by
Electrlc Power Research InstLtute as descrdbed ln Weather
Electricity sales, (I)
;That descriptlon is
Any rnodel- that can be used for weather normallzing monthly sales
can be wrltten in the general form:
to
the
Normallzation of
!EEpE-r""ea uerG:
s
where S
m = f(wrrxr)
m is salee per customer for month rn ;
v,m is vector of rveather measures related to salesin month n; i
x is a vector of non-weather yariables related to
sales in nonth n ; and :
I
f.ls a function of observed eiiplanatory variables rv,
and x
Itr
Given a model of this kindr tceather normaLizatLon of sales
proeeeds as foll"ows. Predicted sales per customer for month
under actual- i
weather condiElon" ,A i"m
s* = r(d,x)
Predlcted sales per customer for nonth m u/rder normal weather
condltion" ,N i"m
Nw
m
x)m'
m
sN = f (
m
Is a measure of normal weather 1n month m..Nwhere wm
Consequently, the predieted adjustment ls 'sales that is requiredto reflect non-normal weather conditlons 1$
(1) ElecEricElectricity
sA)xc.flLcit,rt"T -r""Ih"I
Research Projecc 1922-L.
Normal-lzatlon of
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 31 of 8'l
A_mPower
= (sN_'mResearch
EA-3134,Sa1es t (June 1983,
Intermountain Gas Company
Case No. U-1034-134
Revised Exhlblt No. 1
Page 15 of 65
KohLmeler
I{EATHER NORMALIZATION AND DECLINING USE METHODOLOGY
where C is the number of customers bIlled in month n. Thlsm
adJustment, Arr is applled to the actual- sales for the month of m
to obtaln rrweather normalized salestt. If predlcted sales under
norrnal weather exceeds predlcted sales under actual weather, Ehen
the adjustment is posltive and weather normallzed sales aregreater than actual sales. If predlcted sales under normal
weather ls less than predicted sales under actual weather, Ehen
the adJustrnent is negatlve and weather normallzed sales are l-ess
than actual sales.
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 32 of 81
Intermountaln Gas Company
Case No. U-1034-134
Revised Exhibit No. I
Page 17 of 65
Kohlmeler
I.IEATTIER NORMALIZATION AND DECLINING USE METHODOLOGY
II. E. Decllnlng Usage Adjustment
The regresslon equatlons for the three customer classes RS-I, and
RS-2, and GS-l all Lncl-ude the usage t,rend varlable r.rhichreflects long term decllnlng effects over tine. Thls varlable ishighly statistlcalJ-y sJ.gnlf lcant in al-l- three equations. Infact, usage trend is slgniflcant wlth 992 confldence ln the RS-l
equatlon, wlth 951l confldence In the RS-2 eguatlon, wlth 991l
confldence ln the GS-l equation. A1l" three urodels flt the data
better with the incluslon of the usage Erend than excludlng it oralternatively uslng other trend foruulatlons. Indeed, Ehls trend
can visually be seen to be accurate through the end of avallabledata, aa evldenced 1n the charts of the regresslon equatlon
predicted value versus the actual therms sold.
The relatlonship between this usage trend and therm sales per
custoner is thus a known and measurable effect. Thls effect can
be extrapolated to years followlng tesE year uslng the multipl-e
regresslon equat,lon coefficient and the correct values of the
usage trend varlable. By deflnition, the usage trend variable
increases by one ln each heatlng load month (Novenber-Apri1) and
remai-ns zero in summer monEhs (May-October). Thus the decLinlng
use effects for fiscal study year Oetober 1985-Septernber 1986 for
RS-I are:
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 33 of 81
)
I
Intermountaln Gas Conpany
Case No. U-l034-134
Revised Exhibtt No. I
Page 18 of 65
Kohlmeler
WEATHER NORMALIZATION AND DECLINING USE METHODOLOGY
Usage
Trend CoefflclenE ?herms Effect for YearDate
r0-85
I 1-85
12-85
1-85
2-86
3-86
4-86
5-86
6-86
7-86
8-86
9-86
Date
1-85
2-85
3-85
4-85
5-85
6-8s
7 -8s
8-85
9-85
10-85
I 1-85
L2-85
0
37
38
39
40
4L
42
0
0
0
0
0
Usage
Trend
0
(4.81)
(. r30)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. t30)
(. 130)
(. 130)
Coefflclent
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(. 130)
(t+
(s
(s
(s
(s
.e4)
.07)
,20)
.33)
.46)
0
0
0
0
0
Therms Effect for Year
(30.8r)
(27 .6e)
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 34 of 8'l
We must subtract the declining use effect of the test year from theseeffects to get the net effect to the reported normallzed therms in thetest year. For exanple, lf Ehe test year is January 1985-December-1985:
0
0
0
0
0
0
(4 . 81)(4.e4)
33
34
35
36
0
0
0
0
0
0
37
38
(4.2e)
(4.42)
(4. ss)
(4.68)
Difference (3.12) Therms per Service
whlch ls the decl-inlng use adjustment to the test year,
III.
III.
Data
Intermountain Gas Company
Case No. U-1034-134
Revlsed Exhtblt No. I
Page 19 of 65
Kohlmeler
WEATHER NORMALIZATION AND DECLINING USE METITODOLOGY
A. Data Mnenonics
IHERMS -
I{DD45
HDD65
}tDD45
HDD65
TREND
actual therm usage per service connected during month.
actual- heating degree day (HDD) 45o base.
actual HDD 55o base.
normal HDD 45o base.
normal HDD 55o base.variable which starts at zero, increase by I over wlnter
months (Novenber-Apri1) and is zero ln surnmer months.
Represents trend ln heatlng load rel-ated conservationeffects over study period.
bLnary varlable for January, ls one ln January, zero
otherwlse.
binary varlable for February, is one in Februaryr zero
otherwlse.
- blnary variable for December, ls one 1n December, zero
otherwise.
- gas prLce lagged 2 months.
- actual weather ln nultlple regression equaElon.
- normal weather ln multlple regression equaEion.
- adjustment for normal weather PRED-FIT.
- normal-ized actual therms THERMS-ADJ.
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 35 of 81
Jan.
Feb.
Dec.
GASL2
PRED
rIT
ADJ
NORM
III. B
Intermountaln Gas Conpany
Case No. U-l034-1.34
Revised Exhibit No. 1
Page 20 of 65
KohLmeLer
WEATIIER NORMALIZATION A}ID DECLINING USE METHODOLOGY
Baslc Data
1. SlgnlficanE Data (used ln regresslon or ln normallzatlon).
A. RS-l
'rTRENDrr Usage E::end predictor rvhlch captures
declinlng use effect.
ItocTrr-"APRrr - Monthly binary predlctors.
trTHERltT" - TotaL RS-l Eherns welghted across company
by services ln each divlslon.
rrHDD45Trr - Total- actual heatLng degree days, base 45o,
welghted across company by services in each division.
ItHDD65Trr Total actual heatlng degree days, base
65", weighted across company by servlces in each
divLsion.
rrNDD45Trr - Total normal- heatlng degree days, base 45o,
weighted acroas company by services in each dlvision.
rNDD65Trr - Total normaL heating degree days, base 65"
rveighted across company by servlces ln each dlvlslon.
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 36 of 81
o
o
o
o
o
Intermountaln Gas Company
Case No. U-1034-134
Revised Exhlbit No. 1
Page 2L of 55
Kohlmeier
I.IEATEER NORMATIZATION AND DECLINING USE METHODOLOGY
III. B. Baslc Data (continued)
1. Slgnlflcant Data (used ln regression or 1n normallzatlon) .
B. RS-2
t!TREND" Usage trend predlctor whlch captures
decllnlng use effecE.
ttoclr'rtrDEc"-ttAPRlr - llonthly binary predictors.
ttTHERMTTt - Total RS-l therms welghted across companyby servlces in each dlvlslon.
rrl'lDD4sTrt - Total- actual heatlng degree days, base 45",
welghted across company by servlces ln each divlslon.
trHDD65Trr - Tota1 acEual hearing degree days, base65o, weighted across conpany by servLces ln eachdlvlslon.
rrNDD45Ttf - Total- normal heatlng degree days, base 45o,
weighted across company by servlces in each division.
'rNDD65Trt - Total normal heatlng degree days, base 65"
weighted across company by servlces in each dlvlslon.
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 37 of 81
o
o
o
o
o
o
Intermountain Gas Company
Case No. U-1034-134
Revlsed Exhiblt No. I
Page 22 of 65
Kohlmeler
T'IEATITER NORMALIZATION AND DECLINING USE METHODOLOGY
III. B. Baslc Data (contlnued)
1. Slgnlficant Data (used ln regresslon or ln normali-zation).
C. GS-I (10ts, lIts, and 20re conbtned).
ttTRENDtt - Usage b:end predl.ctor whlch capturesdecllnlng use effect.
rrOCTrr-rrAPRrr - Monthly blnary predictors.
tTTHERMTTT - Total- RS-l therns welghted across company
by services l-n each dlvlston.
rrHDD45Trt - Total actual heating degree days, base 45",
weighted across company by servLces in each divlslon.
ttHDD65T" - Total actual heatlng degree days, base65o, welghted aeross coupany by servlces l-n each
dl-vision.
rrNDD45Trt - TotaL normal heating degree days, base 45",
welghted across company by services Ln each division.
rrNDD65Trr - TotaL normal heating degree days, base 65o
rvelghted across company by services in each dlvision.
Exhibit No. 39
Case No. INT.G-16-02
L. Blattner Reb.
Page 38 of 81
o
o
o
o
o
o
III.B.
Intermountain Gas Company
Case No. U-1034-134
Revlsed Exhibit No. 1
Page 23 of 65
Kohlmeler
WEATIIER NOR}IALIZATION AND DECLINING USE METIIODOLOGY
Basic Data (continued)
2, Non-slgnlficant Data (not used in regression model).
A. Usage Trend Predictors.
o t'TREND|' - Tlme series tncreasing only ln heatlng loadmonths, NOV-APR. (Incl-uded here for comparlson
purposes).
o [TREND2|| - Tlme series lncreaslng only ln heatlng Load
nonths, NOV-MAY.
o rrTIMErt - Tine serles increaslng one each month.
B. Yearl-y Binaries.
C. Monthl-y & Surnmer Binaries.
o JAN-DEC - Value of one in the roonth, 0 tn the others.
o Summer - value of one ln June, July and August and 0in the ot,hers.
D. Average ResldentiaL Electric Pri-ces.
Owyhee Dlvlsionts electric prices were calcul-ated from
Idaho Power Companyts revenues + KWH.
Capltal DivlslonIs electric prices nere caLcul-ated
fron Idaho Power Companyts revenues + KWII.
Sawtooth Divisionf s electrLc prices were cal-cul-aEed by
addlng the prlce of ldaho Powerrs resldential rates x
the welghted IPC usage in the divlslon, to the UEah
Power and Light resldentlal rate x the welghted UP&L
usage and/or Eo the Bonneville Porver Admlnlstratlon
residentlal rate x the welghted usage of BPA Power in
the divlsion.
Skyliners average electric prlces were calculated in
the same manner as Sawtoothrs.
Tetonrs average electric priees were calculated ln the
same manner as SawtooEhts.
Exhibit No. 39
Case No. INT-G-I6-02
L. Blattner Reb.
Page 39 of 81
o
o
o
InEermountatn Gas Company
Case No. U-1034-134
Revlsed Exhibit No. 1
Page 24 of 65
Kohlmeler
I^IEATHER NOR},IAIIZATION AND DECLINING USE UETHODOLOGY
III. B. Baslc Data (contlnued)
E. Average GS-I ELectric Prlces.
Owyhee Dlvislonrs electrlc prlces r{ere caLculated from
Idaho Power Companyrs revenues + KWH.
Capital- Divislonfs electric prices lrere calculated from
Idaho Porver Companyrs revenues + KWH.
Sawtoothrs, Skyllners and Tetonrs average GS electrlcprlces were cal-cuLated In the same manner as thelrresidentlal electrlc prices.
F Unemployment rates tn Idaho by dlvislon as reported by Job
Service.
G. Gross Natlonal Product
H. Reglonal Income }leasures
o
o
o
o ITPERSINCT' - PersonaL lncome as reported ln
Idaho Economic Forecast.
ttPERCAPTt - Per Capita Income as reported ln
Idaho Economic Forecast.
Western Region CPI.
Variations of Gas Price
o ItGASLztt - Gas prlce lagged two months.
o ttPoscAs't RS-l gas price (GASL2) where
posit ive.
o t'NEGGAsrf Rs-l gas prl-ce (cAsL2) where
negative.
o t'GASDL2tr - Deflated gas price for RS-l - GASL2
Market SaturaEl-on Rates.
o tTSATSPACtT - Saturation of space heating unlEs
area.
o rSATI,lATRrr - Saturation of water heaElng unlts
atee.
o
I
J
K
change ls
change ls
+ I{RCPI.
in service
ln servlee
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 40 of 81
RS- I
III. B.
InEermountain Gas Company
Cdse No. U-1034-134
Rdvlsed Exhlbit No. I
PLee 25 of 65
KQhlmeier
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Case No. INT-G-16-02
L. Blattner Reb.
Page 42 ol 81
III. B. 1A
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InEermounEain Gas Company
Cpse No. U-1034-134
Rpvlsed Exhlblr No. I
P'age 27 of 55
Kbhlmeler
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Case No. INT-G-16-02
L. Blattner Reb.
Page 43 of 81
III. B. 1A
IntermounEaln Gas Company
CAse No. U-1034-134
RAvised Exhlblt No. IIPage 28 of 65
Kdhl-meler
Exhibit No. 39
Case No. INT-G-I6-02
L. Blattner Reb.
Page 44 of 81
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CAse No. U-1034-134
R{vtsed Exhlblt No. I
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Kdhlmeier
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III. B. lC
Intermountain Gas Company
Case No. U-1034-134
Revlsed Exhlbit No. I
Page 36 of 65
Kohlmeler
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Case No. lNf-G-l6-02
L. Blattner Reb.
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L. Blattner Reb.
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Intet:mounLain Cas Company
Case No. U-1034-134
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Page 38 of 65
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135. L::Sfl
4E. BOE
t3T. OF:V.
ot3 coEF.
(). 567il
0.039?5
E.4Etl.E.053
1,77e
1. 783
1. (154
1. 6F6
1, ?SS
0. 0{)4/+73
(:,.0(]i17EG
T-RA-i" I U =
CCIEF/S. D.
5. Av
---,1.?L:U. LU
7,'1,1
3.37
5,'j.i
-J.6rt3. i7
-/ e_.i/ r rJ.j
4. EE
Er+. 89
J - L. r-J.U
R-SGLjARED = ?9.8 F1E;1CEN'f
R-SGURRED = 9?.7 trEntrENT, FIDJU5TED FOR D. F.
ANf,LYsI5 ['Ji: Vfi}IIAhICE
F r.r;, if
1 1963E
e9a
1199311
i,15=5!3/ DF
1 i963
FIJ
FURTHER ANALY5IS C,F Vf,RiIINCE
S5 EXPL.T"IiNED BY EftrC}.I VAiIiABLE NhEi{ Ei\,ITEiiED
DUE TT} DF 55
REI3RESS]ON 1Q 1198J;:TRHND 1 67193JAN '. 1€"8,9t+FEB I .3EEE
rYl$]R 1 566Rpn I 33iUCT 1 36
NOV I S6Db.c r. 17EOOt-iDD4s'r 1 SESO
Hilu6ST 1 .f 0J4
Irti ThE OrtDeR E:i.VEi{
f(ES i DLirtrL
-.4. CtJr+
-3. ii;i(l
-4.0954,1i8
-3.3854.37e
5T. REs.
--e.07R
-E. riSR
-1'!'?5.n'oitNo 3e
dseNJ.tlrur:c-to-oz
- E . r-l [.lBtattner Reb.
e, 15RSe 54 of 8'l
Rilt^J
I
lE
t7
LL
4:1;
47
TrrEND
11.t1
(J" (J
JJ.tl
Lg. (l
;:7. ()
0. (J
PRED. Y
VTqLUE
7i1, 519
3E.853
5.3.856
67. E6E
, 1,:l ':' ? EiI J J. LL,U
38. 4;1t)
iiT. Dcv.
PRED.'/
1. O5B
{1. 9I9
i:). 9F, I
L Ctr4
t). Tr15
(). SEFJ
I ! t't .1 '*/1til:t;-t)
DURBiN-t^iATStrN STATISTItr = 1.57 Intermountaln Gas Cornpany
Case No. U-1034-134
Revised Exhlblt No. 1
Page 39 of 65
Kohlmeier
Exhibit No. 39
Case No. INT-G-'1G02
L. Blattner Reb.
Page 55 of 81
III. C. 1B
tylTB i + Ti-ii5 I5 THA FiNRL I'IC}DLL- .:OiI x-s';r1p1i WiA "TilEi\iD. "
MTB ) REGRESS CEE rlN I C;]-EF,. tr18'-tr14. f,*3. tre4,
T}.IE REEi?E$siTiI! EEURTlBN T*1
THERFIT * 5.5(l .F tA.7 JAirl + 1'7.8, Fi:B + 11.7 MFIH +.
-F E,6O NEV + 13.6 DEE + (].C}153 HDD4sT
IntermounEain Gas Company
Case No. U-1034-134
RevLsed Exhibit No. I
Page 40 of 65
Kohlmeier
Alrrl 4. 05 OLT()668 HDD65T
7"L1',+ (:r.
EOLUIYIN {:OEFFIClENT
5.4950
r8.783
17.79.)
I 1, 7E3
7. lf tlt
-4.046g. fir)E
13.558
0. o153Cr6
0.068916
5iT. Di:V.
rll= coEF.
(_). 6()88
E,5?E
E. 154
1, 66(:)
I.54t+
1,175
1. 54:?
e. C,B7
(r. tltt14457
0. 0c)E91 I
'r-,{d1T I C} =f,OEF/S, D,
JFIN
FEB
MAR
APR
081'
NOV
DEC
HDD45T
I.{DD6ET
DUII TO DF
REGRESS I ON !JRESIDUAL E,ETClTftt- 7 t
R0H
6,'.
LT
EE
7t-t
,'1S=S5./DF
1 3EB7
6
ST. IJHV.
.oJ
44
Frg
50
43
64
Ii\,i THE Oi{DER 6JVEi{
i1E5 ]:DUAL
4.7 2/+
-il" ().:;5
4.7 t'e
5,4'ji-t
-|5.'777
,1
7I
7
4
1
6
f
E3
e6
t)G
F,4
5 = 8.379
ft-SBURRED = 9-J.7 Pt--Rf,EN-r
R-SGUARED = 9A.7 pERtrENT. ADJUSTED FOR D. F.
Ri\IALYSi gi 0ir vfiR IfilvtrE
JL,
1 19580
351
1 18930
FURTHER ANflLYgIS T]FJ VAR]ANCE
SEi EXPLAINED PY ERtrFl VARiflBLE l.iHEN Ei\ITERED
DLIE TE DF 55
REI1RHSSJ:EN '3 1IS5BOJAN I .-:i5893Fi::B 1 E/r8?t)MAR 1 7565RpR I 3468ocr .1 ;19NnV J. 3r+51
DfrE 1 34n47.l.llD4IiT 1 5r+66
HDD65T 1 3T6I
i-:1ilED
{)
{)
(-)
(J
I
Ahl
0rl
0(J
{)il
il(-)
(10
J
o.
(-) .
tlt .
rJ.
t:r.
\/I
THERFiT
1(:) 1 . 4Og'27.87':
.s 1 . ;i59
7 L .7917
/+3. /+'lil
PRF.D. Y
VALUE
96.6S5
3E.'7():?
7€,.8,47
66, J(rtl
46. ilr,g
.J',76
386
973
. 351
. t-rOE
ST. rlE5.
,1 . Lgr(
L. UJI\
"1. 17R
8.54R
--r C irO'-' "" Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 56 of 81
n DENOTES r:lN OB5. WITH A LtlR';E Sl', l.1E5,
Intermountain Gas Company
Case No. U-1034-134
RevLsed Exhlbit No. 1
Page 41 of 65
KohLneler
MTB }
MTE }
III. C. lC
# THIS I5 THE FINAI- MODEL FOR RS]OEALL.
REGRESS Cee ON B 81, E3-86, trlE, E14, treJ, CE4.
THE RHERESSION EGUATION I$
THERMT = EJ.6 - (t.t)781 -IREND + J.4.5 JflN + LE.7 FE:B
- 6.93 otrT + 9.44 DEE + (:),0158 HDD4ST +
+9
0.(]
.5e HFIR + 7. 4E 6lPR789 HDDE.ST
TREND
JAN
FEB
MAR
APR
otrT
DEC
HDD45T
HDD65T
DTJE TO DF
REGRESSION 9REBIDURL 6ETOTAL 7L
S = E,478
R-SGUARED = 99.7 PERCENT
R-SAUARED = 99.7 FEREEN]-, ADJUSTED F-OR D.F.
RNALYSIS CIF VRRIANtrE
CI]LUMN
R0t^,
47
E'1
7t:,
COEFFI CIENT
e3.5838
-0. 11761(:t" 1 4.479
1e.736
9. Eel
7.419
-6.9E89.44r
O. C,15171
ct. ct7DE47
ST. DEV.
OF COEF.
0.6196
0, (lJa87
e.318
1,. 9.J7
1 , /1rJ{)
1. 385
1.141t.749
c,. 004376(),(108893
T-RA'fIO =
COEF/S. D.
38. (16
-9,016. i:6
E" 7L
E" Elo
5" 36
'E " t-t'l
5. 4()
3.47
34. gG
sgj
1 48093
341
1 4E404
MS=5S./DF
1578Ct
6
FURTHER ANRLYSIS OF VARIANEE55 EXPLRiNED BY EACH VARIABLE I^IHEN ENTERED
DUE TO DF 5B
REGRESS IoN I 14h-:08,$TREND 1 BLE94JAN t 1914AFEE t 10354MRR 1 750ApR 1 195otrT 1 18DEtr 1 157(18HDD45T L 7C.Ig6HDDGET 7 7t+64
IN TIIE ORDER CIVEN
TRENI)
0.o
J1.0
4e. (:,
Y
THERMT
sB.6S9
86.36E
io6.91€J
PRED. Y
VALUE
63.657
9(1. ,B 1E
7 7. c)4t
$JT" DEV.
PRED, Y
(-,.7(:15
1.081
1.877
rtES I DURL
5. {)3i-::
E.550
-3. (_rH3
s-r, REs.
r-. 1ER
E,49R
-e. J7R
11 DENOTES AN OBS, NI]'H A LARGE ST. RES.
Exhibit No. 39
Case No. INT:G-I6-02
L. Blattner Reb.
Page 57 of 81
DURBIN-WATSON STATIETIC = 1.51
III. C. lD
l{TE } +} THIS TS THE FINA
MTB ) REGRESS trEE BN g C
I{ODEL. FOR RSOEALL h'lo ''TREND. "86, trlE, C14r EE3, tre4.
THE REGRESSION EGUATION IS
THERMT = i:3,7 {- 15.6 JAN + 13. E FEB -}.
+ ,3.87 DEtr {- (J,C)135 HDD45T
IntermounEain Gas Company
Case No. U-1034-134
Revised Exhibit No. I
Page 42 of 65
Kohlmeier
6 MRR + 6.36 APft €,.4? OCl'
, O7BG HDD6E]'
T-RATIO =coEF/s. D,
37.516.78
IN THE ORDER G]VEN
L
B.E+ (:t
CELUMN
JAN
FEB
MAR
RPR
ECT
DEtr
l"lDDztET
HDD65T
ROI^,
a
L9
LL
47
7t:t
trtrEFFI CI ENT
:E3. A967
15.58E
13. E16
8.961
6. E60
-6.4499.87t
*.0i.a4E4
tt. Cr7837g
Cl. t1t1zs3g4
O. C,CIEE4E
ST.
OF
0.
E
1
I
I
I
1
DEV.
COEF.
631 I
. EgB,9e7
. 4115
, 311
, L47
.777
8€,
3g
65
aF"
55
06
()5
a.
E
l+.
't
?r=
S = 8.537
R-S0LJflRIID = 99
R-SGUARED = 9E
ANRLYSIS OF VARIANEE
DUE TN DF
HEBRESSIEN ORESIDUAI- 63TOTAL 7L
7 PERCEN'T7 pERtrENT, ADJUSTED FoR D. F.
ss
14L9gg
408
L4-e4Q4
MS=SS/DF
L773Ct
a
FURTHER ANRLYBIs OF VRRIANCE
SS EXPLRINIED BY EHtrH VARIRBLE WI.IEN ENTERED
DUE TO DF SS
REGRESSION 8 L4T91gJAN L 4T9E8FEB 1 EBg44IYIAR 1 100 17ApR I 5037onT t 47$DEC 1 34937HDD45T 1 1e739HDD65T 7 7gCt7
JAt\.1
o.0(:r
1 . (l(:,
c}. (:)(j
11. r-rrl
r:,. (lC,
Y
THERMT
1S6, C,1E
t39.694
99.613
66,689
6A.gt?,
PRED, Y
VRLUE
1S1. 344
158.04E
?z+.613
63. Q13
73.43A
BT'. DEV.
PRED. Y
t. t)t+7
l. 039
1. 05e
r-r.643
1. (16g
RESi DURL
r+.668
. 6511
5T. REB.g. {)ER
E. (l1R
e.17R
i:. .t 1R
-e.83R
(l0E
678
5e0
4
e.J
E.J
-6
R DENOTES RN OBS. WITH R LRRGE ST, RES.
Exhibit No. 39
Case No. INT.G-16-02
L. Blattner Reb.
Page 58 of 8'l
DUREIN-I^JATSON STATISTIC = 1.43
III. C. lE
IntermounEain Gas Company
casd No. u-1034-134
Rev{sed Exhlbit No. I
Page 43 of 65
Kohlmeler
MTB ) S THIS IS THE FINAL IYIODEL FOR ESALL,
MTB ) REERESS EeE EN 10 Cl,C3-CE!ClE-trl4,C83,trE4,
THE REERESSION EOURTION IS
THERMT = 1.49 - 4.53 TREND +'I7E JRN + E55 FEE + EE6 ftlAR + 164 APR + 68.8 OET+ EEE NOV + E8E DEC .+ rl.3r-r4 HDD45T + rl. E57 HDD65T
COLUMN
TREND
JAN
FEB
MRR
APR
OCT
NOV
DEC
HDD45T
HDD65T
trOEFF I EI ENT
L48.73L
-4,8343
E.72,47
EEg, C)Ege5. gg
183.60
6s. 84
EEE. E7
888.41
0.3c,449o.85785
ST. DEV.oF uoEF.
6.516
0.4gcl6
e.9. L9
84.90
E1. g6
E1. EB
13. E8
19. 83
Ezr. EJ
c,. c)5373().(]3968
T-RATIO =
trOEF/S. D,
81 . 5(t
-9. zr3
9.58
10. 4(l
1C,. 63g.6e
5. 18
11. 17
11.66
3. g7
7. A7
$ = E6.Eg
R.SOUARED
R-S6IUARED
ANALYSIS BF VARIANtrE
DUE TO DF
RECRESSICIN 1I]RESIDUAL F.TTOTAL 7L
9.1 PERCENT
B, I pEREENT, RDJUSTED FOR D, F.=$=g
S$
4583355
43r.t 1C)
45?6365
lvlS=SS/DF
455335
TCtg
FURTHER ANALYSIS trF VARIANEE
S5 EXPLRINED BY EAtrH VRRIABLE WHEN ENTERED
DUE TO I]F S5
REGRESSIONT 10 4553355TREND 1 E T499lE1.J0N L A66eC)5FEB 1 343488MAR I 16935ApR 1 4368eoc-f I E4L3(tNT]V 1 7EE5DEtr I 1(,6561 EHDD45'[ 1 199546HDII6ST L 437tlt)
IN THE ORDER 61VEN
REW
19
EO
E8
3(t
TREND
15. (,
16. tl
O. ()
E(:).0
Y
THERMT
E7 r .76
871.9(]
38Ct. E/+
783.74
PRED. Y
VALIJE
?09. (:)E
I31 1 . 4t)
'1':.? EDgEsJr gJ'730.40
$T. DEV.
PRED. Y
11.57
1E,17
1(:). 87
11.14
RES I DUAI.-
F,e.74
6(1, 5(,
56.65
53,34
S1" RES.
T" E,3R
!-r E/- nr:. JE fI
e.34R
E.ElR
R DENOTES AN OBS. WITH FI LARGE ST. RES.Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 59 of 81DUREIN-WATSON STATISTIC = .3A
III. C. lF
InEermountain Gas Company
Cas€ No. U-1034-134
Revlsed Exhiblr No. 1pae& 44 of 65
Kohlmel-er
MTE ) # THIS IS THE FINAL MODEL FOR 6SALL U/O ''TREND
MTB ) REGRESS trEE trN B tr3-tr6, C1E-tr14, trE3, CE4,
THE REERESSIEN EBUATION IS
THERMT = 1/+E + E6€ JAN + EI.4 FEB + 13E MAR + 86. 1 APR + 66.7 TJCT + 133 NOV+ E38 DEtr + 11. 116 HDD45T + 0, EgCr HDD65T
:
I
I
I
JAN
FEB
MAR
APR
ot:T
NOV
DEC
HDD45T
HDD65T
DUE TO DFREGRESSIIlN 9RESIDUAL E,ETtrTRL 7I
5 = 4'1.3C1
R-EGUHRED = 97.7 PERCENT
R-StrURRED * 97,4 FEREENT, ADJUSTED FER D, F.
ANALYSIS OF VARIANCE
CCILUI{N
ROh'
LE
eo
?L
67
6g
69
trOEFFICIENT
144. 89g6E, 14
813.5E
131 , 7E
66, 1e
68.74
138. 8(r
E;7.63
cr.11613
c,. EgCl l tl
ST. DEV,
OF trtrEF.
1rlt.74
48. 31
37. gE
E9. 19
86. 87
Er]. 66
97. Ee
36. 96
Cr. t:t77Gt:,
Q.05C)54
T-RATIO =COEF/S. D.
13.4?
3.79
5.6E
4, 51
E. /+6
g. rg
4. gg
6.4d
1. 5(t
3,7 4
ss
44.90590
tQ5773
r+E963EE
MS=SS/DF
498944
t7Q6
FURTFIER RNRLYSIS OF VRRIRNCE
SS EXPLAINED BY EAtrH VRRIABLE h,HEN EN'IERED
DUE TO DF SS
REGRESSIIIN I 449439Ct
JRN 1 13334E9FEH I 668l erlMAR 1 E3B3O4ApR 1 51373tlcT I E 18NOV 1 EEAA4TDEC 1 159853EHDD4ST 1 18.t768
HDD651' I 56813
IN THE ORDER GIVEN
JAN
1. QCr
(:1. (:xj
E). t:tQ
1. t)O
(:r. o0(1.00
Y
THERMT
97 L.76
871. 90
6E6.35
9Cxl. ()B
591.34
389.66
PRED. Y
V(lLUE
87L.41
768. *tl
345,.7t+
E7e.7E
a87. AB
477.37
ST. DEV.
PRED. \y'
16. 5r-t
17. 11
J,6.-BB
81. 04
19.37
18. E4
RES I DUAL
1oil.35
ICtg.717
6r)" 6E
-'73. F,3
-1A.54-88.91
ST. RES.
9.6,6R
E.9ER
e. 14R
-E, rl'7R
-E. 61 R
_;3. /1r)R
R DENOTEB AN OEs. I{I'TH A LARGE 5T. RES.Exhibit No. 39
Case No. INT-G-I6-02
L. Blattner Reb.
Page 60 of 81DURBIN-WATSON STATISTIC = ,38
1
Intermounta
Case No. U-Exhlblt No.
Page 45 of
Kohlmeler
n Gas Company
034-134
I
residual variaEion
ssion equation
i
I
6s
I
i
I
1II. C
WEATIIER NORMALIZATION AND DECLINING USE METHODOLOGY
Models and Predlcted Results.
1. Mode]-s
A. RS-l Model-.
RS-1 Model Wlthout Usage ItTREND" Variable.
RS-2 Mode1.
RS-2 Model Without Usage I'TREND" lariable.
GS-I Model.
I
GS-I ModeL l,Ilthout Usage t'TREND" VarlabLe.
Predlcted Results.
A. RS-l
o ttTIIERMstt - AcEual Therms.
"REsIDtt - Residual-s fron Mu1tiple Regressi.on.
o rrPREDltt - Actual l,leat,her 1n }lultlple Regression
Equation.
;
o llflTlrr - Norma1 l,Ieather in llultiple Regression
Equatton.
B
c
D
E
F
2
o
I
I
!e
I
!
Is
o
rrBJrr - Box Jenkins predlction of
adJustment.
"PRED2tt - Actual weather in regr
adJusted by Box Jenklns.
o tttr'IT2rr - Nornal weather in regre
by Box Jenlcins.
slon equatlon adjusted
o
ttADJ2tt - Normal weather adjustment (PRED2 - rrr2).
(THERMS + ADJ2).
Exhibit No. 39
Case No. INT-G-'|6-02
L. Blattner Reb.
Page 61 of81
rrNoRl4r' - NornaLlzed actual therms
Intermounta
Case No. U-Exhlblt No.
Page 46 of
Kohlmeler
III. C. Models and Predlcted Resul-ts. (conElnued)
B. RS-z
o rrTHERI"lsr' - Actual Therms.
Iinli0,1
ds
I
I
Gas Company
34-134
I^IEATHER NORMALIZATION AND DECLINING USE METHODOLOGY
o ttREsIDrr - Reslduale from Multlple Regresslon.
o ItPREDlrr - Actual Weather in Mul-t.lple Regression
Equation.
Io rrFITIrr - NormaL lleather in I'luJ.tipJ-e Regresslon
EquatJ.on.
o rrBJrr - Box Jenklns predlctlon oft residual variation
adJustment.
o rrPRED2rr - Actual weather Ln regr'esslon equation
adJusted by Box Jenklns. ,
o rrFIT2tr - Normal weather in regrebslon equation adJusted
Iby Box Jenklns.
o rrADJ2rr - Normal weather adJustraent (PRED2 - FIT2).
o trNolurt - Normalized actual therms (THERMS + ADJ2).
Exhibit No. 39
Case No. INT-:G-16-02
L. Blattner Reb.
Page 62 of 81
Intermounta
Case No. U-Exhibit No.
Page 47 of
Kohlneier
o rrBJrt - Box Jenkins predictLon of
adJustnent.
{n Gas Company
il034-r34I
ols
III. C
I,IEATTIER NORMALIZATION AND DECLINING USE METHODOLOGY
Models and Predlcted ResulEs. (contlnued)
C. GS-I
o "TlIERMsrt - Actual Therms.
o tTRESID" - ResiduaLs from l"lultlplp Regression.
o trPREDlrr - Actual l,Ieather ln I'Iultlp1e RegresslonEquation.
r
o ItFITlrr - Nornal t+eather ln Multiile Regresslon
Equat,Lon.
o ltPBED2r' - Actual weather in
adJusted by Box Jenkins.
residual- varlaEion
slon equatl.onregres
o rrFIT2rr - Normal weather in regrebsion equatlon adjusted
by Box Jenklns.
o rrADJ2rr - Normal weather adJustment (PRED2 - FIT2).
o rrNoRMir - Normallzed actual thermp (TIIERMS + ADJ2).
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
Page 63 of 81
RS- 1
III. C. 24.
MTB )
ROW
PRINT EEl-trE5RESID PIIED1
InEermounEain Gas Company
Cdse No. U-1034-134
RAvised Exhibtt No. I
Plge 48 of 65
K6hlmeier
i
PRE,DE
t
g
3
4
6
7II
I r-)
11
1E
13t4
1E
16
L7
1B
19
Er-r
E1
AE
E3
e4g5
E5
e7
EB
E9
EO
31
3E
??
34
35
36
e7
38
39
40
4L
4E
43
44
43
4A
47
4B
49
5(l
51
5E
53
34
55
e,E
-t.
1
1
3
4
c)
-1
I
(lt '1.
'3
-0.
E'
-C).
-1,
E.
-1.-1.
-4.-0.0,
-o.
1.
E.
0.
-q-4.
1.
-0.
?t:f E:7.=l
3ACr4e
973.34
(',8979
5,T733
987-ca
16948
43S1 1
o3335
t3crg7
96398
EA97t
egsE6
64815
80315
EeC,t]9
09550
54183
91615
45433
45E66
1E887
77||)A8
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III. C. 2B
Intermountatn Gas Company
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III. C. 2B
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Kdhlmeier
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Case No. INT-G-16-02
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Caise No. U-I034-134
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Kdhlmeier
Exhibit No. 39
Case No. INT-G-16-02
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Intermountain Gas Company
Case No. U-f034-134
Revised Exhiblt No. I
Page 61 of 65
Kohfuneier
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Case No. U-1034-134
Revised Exhiblt No. I
Page 62 of 65
Kohlmeier
Exhibit No. 39
Case No. INT:G-16-02
L. Blattner Reb.
Page 78 of 81
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Case No. U-1034-134
Revised Exhlblt No. I
Page 63 of 65
Kohlmeier
Exhibit No. 39
Case No. INT.G-16-02
L. Blattner Reb.
Page 79 of 81
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Case No. U-1034-134
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Page 64 of 65
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Case No. INT.G-16-02
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Case No. U-1034-134
Revised Exhiblt No. I
Page 65 of 65
KohLneler
Exhibit No. 39
Case No. INT-G-16-02
L. Blattner Reb.
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