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Energy Pq,be I Enqu6te Eneraie
an evalUation
afthe
ISER electricity demand forecast
July 30,1980
Robert E.Crow
James R.Mars
Christopher Conway
TIC.
1425
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KlA OE4
)233-0260
43 Queen's Park Crescent East •
Toronto,Ontario.Canada,
M5S 2C3.
(416)978-7014
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an evaluation
of the
ISER electricity demand forecast
July 30.198'0.
Robert E.Crow
James H.Mars
Christopher Conway
ARLIS
Alaska Resources
Library &Information Services
Anchorage j Alaska '.
53 Queen Street.
Ottawa.Ontario.Canada t
KIA OE4
(613)233-0260
43 QueenlsPark Crescent East.
Toronto.Ontario.Canada.
M5S 2C3.
(416)978-7014
'.
1'•.
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In December 1979~Energy Probe was awarded a
contract by The House Power Alter nat iv es Study Com-
mittee of The Alaska State Legislature to examine and
evaluate an electricity demand forecasting model
being developed by The University of Alaska's Inst-
itute of Social and Economic Research (ISER).
Energy Probe's work~along with research carried
out by several other consultants retained by The
Power Alternatives Study Committee was intendegito
provide a framework within which the proposed Susitna
Hydroelectric Power Development could be evaluated.
A working paper published in January 1980 presented
an initial evaluation of the ISER model>primarily
on the basis of ISER's ltDetailed Work Plan ll
•
The following is the final report prepared under
Energy Probe's contract.It presents an evaluation
of the ISER demand forecasting model in its present
form;tests the sensitivity of Railbelt electricity
demand to changes in various policy and technological
factors;and outlines what the authors believe to
be the aP0ropriate interpretation 8Md application of
the fOl"ecast \'/Hhin thE'broader context of State
energy pol icy development.
The views and conclusions presented herein are
those of the authors aliAie,and do not nE;cessad1y
reflect the position of The House Power Alternatives
Study Committee.
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TABLE OF CONTENTS
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1.INTRODUCTION 10 -'"'".,;..1
2.A USER'S GUIDE TO THE ISER FORECASTING MODEL .....•.•....4
2.1 Introduction
2.2 Stage 1 Components
2.2.1 The MAP Econometric Model
2.2.2 The Household Formation Model
2.2.3 The Regional~Allocation Model
2.2.4 The Housing Stock Model
2.2.5 Stage I Summary
2.3 Stage II:The Electricity End Use Model
2.3.1 The Residential Sector
2.3.2 The Commercial-Industrial-Government Sector
2.3.3 Stage II Summary
3.A TECHNI CAL REVIEW OF THE ISER FORECASTI NG METHOD .•.~.,20
3.1 Introduction
3.2 MAP
3.3 The Household Formation Model
3.4 The Regional Allocation Model
3.5 The Housing StDck Model
3.6 The Electricity End Use Model -Residential Sector
3.7 The Electricity End Use Model -CIG Sector
3.8 Summary
4.AN ANALYSIS OF THE ISER ~lODEL OUTPUT .........................'"..28
4.1 Introduction
4.2 Case "A"End Use Scenario
4.2.1 Residential Space Heating Requirements
4.2.2 Major Resident 1al Appl iance Energy Requirements
4 .•2.3 Unspec ifl ed Residential Appl i~nce Requirements
4.2.4 Residential Summary
4.2.5 The Commercial-Industrial-Government Sector
4.2.6 Summary of Case "All Scenario
4.3 Case "B"End Use Scenario
4.3.1 Residential Space Heating -Requirements
4.3.2 Major R~s1~~ntial Appliance Energy Requirements
4.3.3 UnspeciffedResident1al Appliance Requirements
4.3.4 Residential Summary
4.3.5 The Commercial-Industrial-Government Sector
4.3.6 Summary of Case 118 11 Scenario
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5.MAJOR CONCLUSIONS AND·RECOMMENDATIONS ~44
5.1 General Commentary
5.2 Funding For Load Forecasting Research
5.3 ISER Model Automation
5.4 Fu ture Use of the ISER Forecast
5•5 Da ta Co 11 ec t ion
5.6 Statewide E1 ectric ity Demand!Forecast;ng
5.7 Peak Demand Forecasting .
5.8 Additional Stage I Sce~ario
5.9 Indepehdent £xpert Advice on the Load Forecast
APPENDIX .............................•........"".....A-l
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1.INTRODUCTION
The electricity demand forecasting model developed by
the Institute for Social and Economic Research (ISER)is a
major step forward for Alaskan energy planning.The ISER
model is of a qualftywhich is orders of magnitude ahead of
previous forecasting models employed in the State.
This report seeks to accomplish three tasks.The first
of these in an introduction to the structure and logic of
the ISER model aimed at a non-technical audience.The second
is a technical review of the ISER model with a focus on the
methods employed and areas for further development.The third
is a deilionstration of the use of the model in documenting
the effects of alternative energy policy assumptions on the
mod el IS ou tput.
By far the most important of these is the third.Since
Alaska's electricity future is not fixed but rather subject to
both fate and pol icy i ntervent ion it is important to a pprec iate
that any forecast depends on assumptions concerning factors
Which can and cannot be controlled.,
1.
2.
On the fate side of the ledger are all those factors which
are beyond the control of Alaskans.These include national economic
pol icy to the extent that it sets the tone.for state economic
and social development and,more importantly,the future of
resource di scovery ,and expl oitati on in Al aska.
Manageable factors include the ways in which Alaskans
actually use the energy which is available to them -whether
they use is efficiently or inefficiently.A very clear example
of the "rna nagea bil Hi'of these factors is the recent energy
conservation legislation which will undoubtedly influence
energy use in the Stat~.
Planning 'is a process by vihich those fClctors \'ihich are,
controllable are identified and managed to bring about a desirable
future.In addition,planning seeks to identify items subject
to fate to adequately prepare for the realization of a range of
possible outcomes.A forecasting model is nothing other than
an aid to clear thinking in this complex situation.A good
forecasting model should be abl e to accommodate both controllabl e
and non-controllable factors and·progress logically to actual
numeric forecasts.On this count the ISER model is exemplary.
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3.
In any forecasting environment assumpti.ons are crucial;
to the extent that they are hidden there is no clear link between
policy and actual outcomes.Toth€extent that they are open
and accessible they are the basis for analysis and action.On
thiscoontas well,the ISER model is excellent.Assumptions
are clearly stated and readily changed.When the model is
ultimately computerized the latter will become even easier
and the model even more useful.
But what is most important to realize is that the ISER model
is only a tool.Alaskans do to a large extent have control over
many aspects of their energy future.In an appropriate planning
env'ironrnent,the ISER model can be utilized to suggest means
of making that future more desirable.
2.A USER'S GUIDE TO THEISER FORECASTING MODEL
The lSER el ectric ity demand forecasti ng model,whil e
seeminglycompl ex,has a very stra'ightforward and logical
structure and flow of information between components.The
output of the model is projected values of electricity·
consumption for each of the three geographical areas of the
4.
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Railbelt classified by final use (i.e.heating,lighting,etc.)·
and consuming sector (commercial,residential,etc.).In its
current form the ISER model produces values for the years
1985,1990,1995,2000 j 2005 and 2010.
To accomplish this task the model relies on five special-
ized sub-models linked by key variables,and driven by policy
and technical assumptions and state and national trerlds.A
flow diagram showing the sub-models and their linking and
driving variables is given in Figure 2.1 below.Of the
five sUb-models~only the MAP econometric model was in existence
prior to the Railbelt study;the remaining four were developed
by ISER during the course of the study.
2.2 Stage I Components
In our earl ier working paper (contained as the appendix
to this report)we argued that the electricity demand fore-
casting process was essentially two-stage.In Stage I,basic
1 't\II,
1
t •
r ...
ngure 2.1:A SCh_t,C ;;-;he ISER Electricity Demand Fore:st _1
US economic trends
kW.h cons-
umpt ion by
end use and
sector over
time
fuel market
shares·I
utilization I
employment for 3
areas
-
\1
households
'J I--household
~head shi p ELECTRICITY
'JG STOCK housing END -USE
stock split ...MODEL -~)EL
popul at ion
by regi on !I--
,j ...I
po pu 1at ion and
survey on ~
housing ChOifL:..HOUSI
I...t·10
incomes .__
-resource extraction rJS and State
scenarios t deillOgraphic
t\'endsstategovernment
decisions ._~
state populationr---HOUSEHOLD
by age and sex3ll-i FORMATI a
MODEL
MAP
ECONOMETRIC
MODEL
i ~REG~;L
!employment by 1 ALLOCATI 0
.sector and MODEL
po pu 1at ion •
l "'___~~Jl I
STAGE I STAGE II
(J1
economic and demographic information is developed as input to
an electricity demand model which we called Stage II.The
final ISER model has this basic structure with the t~AP,
household formation,housing stock,and regional allocation
model s performing the Stage I function and the el ectricity
end use models in the Stage II role.
"~~J.~lhe MAP Econometric Model
The basis of the Stage I function in the ISER model is
,
MAP.a medium size econometric model which translates forecasted
or assumed levels of national economic trends,statE govern-
ment activity,and developments in the Alaska resource sector
into forecasted levels of statewide population by age and sex,
(-'l1lployrnent by industrial sector,and income.Hhile the MAP
model 'is internally complex,its basic logic is that the
State of Alaska will tend to follow national trends in economic
deve10pment yet will deviate somewhat with resource sector
and state government actiVity.These will cause the state to
perform somewhat better or worse than the Outside.In periods
of pl enty~Alaska will attract immigrants seeki ng employment
opportunities;in periods of relatively poor economic perform-
ance.peopl e will tend to 1 eave the State to seek opportuniti es
in the lower-48.
As a result of this basic logic~MAp·s output is quite
sensitive to the national trends~'~esource activiiy.and state
government actions assumed as input.Since MAP inputs directly
6.
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7•
into the electricity end use model,the final results of the
forecasting process are equal1y sensitive to these crucial
assumpt ions.
MAP's output,while technically quite reasonable,is not
appropriate for direct input into the electricity model for
two reasons.The first of these is that MAP produces forecasts
for the entire state of which the Railbelt and its component
areas are only a part,a.lbeit an important one.Secondly,
electricity consumption is more closely related to tlOuseholds
and the number of housing units than to the number of individuals
in the market area;MAP produces only the latter.The household
formation,housing stock,and regional allocation models
translate MAP output into final Stage I form.
The hou::.(~hold for'lllation model groups individuals into
household units on the basis of national and state demographic
trends~The basic logic of this model is than an individual
has a finite chance of being a househol~head;the probability
of headship depends on the individual's age and sex.
Applying these probabilities to MAP's output yields the number
of hOllseholds~a critical input into the electricity end use
model,and the number of household heads by age and sex,an
input into the housing stock model.
8.
The purpose of the regional allocation model is to allocate
MApis statewide forecasts of population to the regiens .of the
Rail belt.The inherent logic of this model is that regional
population shares are sensitive to employment opportunities in
the various l~egions.These opportunities in turn depend on which
industrial sector is predominant in the MAP forecast,and its
likely location.The regional allocation model ultimately
disaggregates MApls statewide forecasts of employment and
population into regional shares.This information serVes as
input into both the housing stock model and the electricity
end use model.
Because heating of residences is an important use of
electricity in the Rililbe1t;and because there are a number
of different types of housing available (single family,duplex,
apartments and mobil e homes)it is necessary to forecast the
numbers of each type of dwelling unit i.n each of the Railbelt
regions.This task is accomplished in the housing stock model
which combines the hOUSehold headship information from the
household formation model,the regional population informati6n
from the regionaf allocation model ~and the results of an
independent survey on housing choice~to produce the number
of housi n9 units by type and region foreac h of the forecast
years.
,-
~
.}
'.
The logic of the housing stock model is quite similar to
that of the household formation model.After combining the
household and population information to produce the number of
households per region over the forecast period,the information
on housing choice is applied to assign each household to a
dwelling.The assignment is based on the probability that a
household head of a particular age and sex will choose to live
in either a single family,duplex,apartment or mobile housing
units by type and region over the forecast period.
In summary.the Stage I portion of the e1 ectricity demand
forecasting pn1ccoss is handled in the ISER node1 by r·jAP,the
household formation mod~l ,the regional allocation model,and
the hous;ng stock model.MAP produces forecasts of st~t~~'0_i.~
employment,population and income on the basis of national
economic trends.acticUy in the Alaska resource sector.and
state government policy.The household formation model .groups
individuals into household units on the basis of state and
nat'ional demographic trends.The regional allocation model
assigns a portion of statewide population and employment to the
regions of the Railbe1t on the basis of the location of
projected economic activity.The housing stock model produces
forecasted counts of dwell ing units by type on the basis of t.he
output of the household formation model~the regional allocation
9.
model,and a survey of housing choices.
The regionally disaggregated employment~population and
housing information is then passed forward to the electricity
end use model for translation into projected requirements for
electricity in the Railbelt.
Assumptions playa central role in determining the overall
output of the Stage 1 part of ISERts model.While the most
important of these are nati ona 1 economic trends,resource
sector activities~and state government decisions which drive
MAP,there are in addition national and state demographic
trends and housing choice information which ultimately
influence electricity consumption forecasts for space and water
heating,and for other residential uses.Critical among these
are the assumptions which lead to projections of household
size:should these prove incorrect,or for that matter,should
any assumption in the model prove incorrect,then the forecast
as a whole becomes somewhat suspect.
2.3 Stage 11:The Electricity End Use Model
The ISER electricity end use model translates the Stage I
output into estimates of the final demand for electricity for.
each region and consuming sector in the Rail belt.The basic
logic of virtualli all components of ISER 's Stage II model is
that electricity is used in identifiable activities such as
cooking,heating a building,etc.Each activity has an observed
10.
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electricity "intensity",that is.a quantity of electrical
energy required to fuel a single unit of the activity in
question.Furner,these intensities are subject ~o change
over time.Combining this information with the output of
Stilge I,which projects the magnitude of specific activities
over the forecast period,yields projections of electricity
requirements for each activity in each region.These may be
summed to.give final forecast estimates.
Consider,for example,the activity of refrigeration.In
1980,a "typical"refrigerator in the Railbelt used about 1250
kWh per year.Over time this average intensity changes as
older,smaller,manual-defrost models are replaced by newer,
lar-ger,forst-free units.Suppose,hypothetically.that a
typical refrirerator in service in the year 1995 uses 1800 kWh
annually as Cl result of fHteen 'years of replacelnent of i'lOrn
out units with new large units and purchases of new units by
newly formed households.If there are,say~15;000 households
forecasted to the located in the Fairbanks region in 1995 then
the total energy requirement for refrigeration in Fairbanks
in 1995 is 1800 kW+ousehold x 15000 households,or 27,000.000
kWh,assuming that each household hasa.refrigerator.
In actual fact,the ISER method does not work this way
mechanically;however 9 logica.lly and mathematically ISERls
model follows this basic procedure for nearly all activities.
11.
In the residential sector~ISER has identified seventeen
separate activities for analysis.These are:
1.heating a s'ingle family home
2.heating a duplex
3.heating a multi-family unit
4.heating a mobile home
5.powering a water heater for general hot water needs
6.powering a water heater for hot water input into a
dishwaSher
7.powering a water heater for hot water input into a
washing machine
8.powering an electric range
9.powering a clothes dryer
10.powering a refrigerator
11.powering a freezer
12.powering a television set
13.powering an air conditioner
14.powering a dishwasher exclusive of hot water needs
15.powering a washing machine exclusive of hot water needs
16.powering lights
17.powering small,unspecified appliances
In the model,activities 5 through 15 are treated similarly
as they relate to energy for large appliances.Activities 1
through 4 are also similar as they deal with space heating.
ActiVities 16 and 17 are dealt with as special cases.
In space heating)the basic unit of analysis is the
individual beating platit of the dwelling unit.For ~n elect-
rically heated dwelling unit this means either an electric
furnace.a collection of baseboard or ceil 1ng resistance units.
or an electric heat pump.ISER has assumed the latter to be
insignificant over the forecast period.Heating plants are
classified according to their "vintage".that is~their period
of installation.There are seven vintages of heating units,
12.
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.-
13.
pre-1980,1981 -85.1986 90,and so on.
Each vintage of heating plant has its own average electricity
requirement which is based on the size.construction.and
"re trofitll potential of the dwelling unit into which it was
originally installed.For units built in 1980 and before.
average consumption is simply the observed consumption of
eXisting units with no conservation or retrofit over time.For
new units t average consum~tion is the product of four terms:
a base consumption level.a ho~sing unit size coefficient,a
co'nservat ion coeffici ent.and a r~trofit coeffic i ent..The
base level gives the consumption of a typical electric unit
currently being produced.The size coefficient factors this
up over time to account forincreas'ing dwelling unit si.zes.
The conservation coeffi ci ent factors the product down to
account for improved heating techniques;~nd the retrofit
factor further reduces this product to account for improvements
to the dwelling unitls efficiency over the life of the heating
plant.The average consumption of an electric heating plant
can,therefore,increase or decrease with ne\'Jer vintages
depending on the assumptions made concerning base level consump-
tion and the relative strengths of conservation and retrofit
as opposed to increasing unit size.
Heating plants in the ISERmode"'wear out over time,
according to an expected lifetime schedule.A heating plant
has an expl ic it probabil ity of II survivi ng ll from one forecast
year to the next,which depends on the age of the heating unit.
14.
For example,the probability that a heating plant installed in
1980 will still be in service in 1985 is much higher than the
probability that a heating pl~nt installed in 1980 will be
in service in the year 2000.
When a heating plant IIdies",the model assumes that,in
effect,the housing unit dies with it.The heating unit is
replaced with either an electric or non-electric heating
plant according to specified probabilities of "cap ture ll which
run on the order of 9:1 in favour of non-electric units.If
an electric heating plant is chosen~it is of the average
consumption appropriate to the vintage of the replacement
period.This assumes for allinte,nts and purposes that either
the dwelling unit itself is replaced with a new unit or that
the dwelling undergoes major alterations to increase its size
to approximate that of currently produced units.
There is a "logic prOblem in th"is case which will be
discussed in our technical review.Basically,the problem is
that the replacement of electric units by non-electric units
is likE!ly overstated as is the alleged "growth"of units which
switch from one electric heating plant to another in a partic-
ular period.In terms of electricity requirements.these tend
to offset one another,to an unknown extent.We will assume
that they offset one another exactly for the purposes of our
subsequent analysis;however,we strongly recommend that the
space heating section of the ISER model be reformulated in
terms of dwellings rather than heating plants to more accurately
r efl ect rea 1 Hy.
In operation the ISER electricity end use model accepts as
input the number of dwell ing units by type from the housing
stock model of Stage I and works recursively through the
forecast period by vintage.For a given forecast year.the
difference between housing units required and those "surviving"
from previous periods constitute new housing starts.The
number of these which are electric is mu1tipl ied by the average
consumption of electric unitt of the new vintage~together
with the total consumption of previously built el ectric units.
this given el ectric space heating requirements for the
forecast year.
Assumptions again are critical at this stage in the model.
"The most important are the relative effects of incrci:Jsir,g
size as compared to conservation and retrofit potential;
aclditiunally,Uie r'elative ;'ca.pturel!of electde as opposed
to oil or gas heating is quite important.
For majorappl iances,the ISER el ectricity end use model
follows a structure similar to that of the space heating
segment.Each appliance is classified according to its
vintage.for each Vintage the average consumption is computed
as the product of base level consumption,a size factor and a
conservation factor.The appliances follow a survival schedule
similar to that of heating plants;the number of appliances of
a particular type in service at a point in time is the number
15.
16.
of households times the probability that a household will own
the appliance.In some cases,this probability is close to
1 already;for others it is more modest but is assumed to
grow over the forecast peri od.
General water heating,for purposes other than clothes or
dishwBshing is adjusted downward to account for diminishing
household size.Where alternative fuels exist,an explicit
assumption is introduced to determine the electrical share.
Operationally,the model determines required additions to
the appl lance stock by subtracting required stock in a forecast
year from "surviving"units from previous periods.As in the
space heating model,the total energy consumption is the sum
of the numbers of units of each vintage mu1tipl~er b:,the
appropriate energy intensity per unit.
The remain'ing activities in the residential sector are
lighting and powering small appliances.The ISER model assumes
a constant electricity requirement of 1000 kWh per unit annually
for 1ighting.This level is assumed constant over the forecast
period with increasing lighting requirements arising from
increased dwelling size offset by conservation and technical
improvements in the efficiency of 1ighting devices.Small
appliances begin with q base requirement (in Anchorage,this
is 1010 kWh per year per housing unit),and grow by a constant
amount in each five year forecast period to accommodate
expanded use of existing small devices as well as the use of
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new small appl iances which may come into service over the
forecast period.
In summary.the residential portion of ISER's electricity
end use model operates on seventeen identifiable activities.
With the exception of lighting and small appliances,the model
works with discrete vintages of consuming devices.It intro-
duces expl icit assumptions about the'el1,~rgy intensity and
survival charact~ristics of each d~vice and vintijge and calcu-
lates the numbers of each vintage in service on the basis of
output of the Stage I process.and,where appropriate.
explicit assumptions about electricity's share and the
proportion of households owning a particular energy using
device.
2.3.2 The Commercial-Industrial-Government Sector
Because of .data shortages the ISER electricity end use
model is rather thin in the eIG sector.While there are
certainly as many specific activities using el ectricity in
this sector as in the residential sector,they are unknown at
the present time.Consequently,the ISER model takes a
"second best"approach to modelling el ectricity requirements
for the CIG sector.
17 .
In the CIG portion of the end use model there is effectively
only one activity,providing all the electricity required for
a eIG employee to carry out his or her job.Included,or
•rather subsumed by this classification are lighting,heating,
equipment op(~ration,and a11 of the other activities specific
to employment.
The CIG portion of the model employs a structure similar·
to that of heating and major appliances in the residential
sector.Jobs are of one of seven vintages,depending on their
creation date which is in turn related to the estimates of.
employment originating in the MAP model and allocated to
.regions by the regional allocation model.The basic logic
is that the energy intensity of a particular job depends on
the technology in place at the time of its creation;the job
maintains essentially the same energy intensity over the
forecast period although conservation may be factored in
over time.
Explicit assumptions about per job energy intensities are
a central feature of the eIG portion of the model;in ISER's
forecast these are projected to grow nearly three-fold over
the forecast period.Jobs created in the 2005 -2010 period
require about 30,000 kWh per year in the Anchorage region as
compared to about 10,000 kWh per year for jobs created before
1980.
18 .
Operationally,the eIG model is virtually identical to the
residential model except that it is driven by employment rather.
than the number of households.For a given forecast year,
employment gror/th is calculated by subtracting
1 g.
existing employment from total employment.Energy intensities
specific to the respective "vintages l1 of jobs are applied
and the results summed to give overall eIG electricity
requirements.
Because of the aggregate nature of eIG activity in the
model,it is virtually impossible to identify all the assumptions
upon which it is based.The actual parameters used in the
forecast indicate that ISERwas quite conservative in working
with this portion of the model;a large amount of electricity
growth per employee is foreseen.However,it is not clear in
which of the specific activities of employment the growth is
to occur.
The Stage II function of the ISER forecast method accepts
input from Stage I and translates this information into detailed
projections of electricity requirements for each region of..
the Rail belt.The electricity end use model developed by
ISER identified 18 -electricityusingaetivities,of which 17
are in the residential sector and 1.in theeommercial-
industrial-government sector.The model forecasts on the
basis of the vintages of consuming devices..Expl feit assumptions
regarding numbers of devices in operations energy intensity,
and electricity's share of the fuel market are introc:;1uc~d
where appropriate.
20.
3.A TECHNICAL REVIEW OF THE ISER FORECASTING MET~OD
3.1 Introduction
Pri or to the development of ISER's el ectri city forecasti ng
model~both ISER and Energy Probe agreed that the goal of ISER's
research should be the development of an "econometric end-use"
(EEU)forecasting model.The name is derived from econometric
methods,which employ statistical techniques to estimate the
effects of price~income,and other pertinent factors on demand.
employment~or population change,and end-use methods,which
seek to explain energy use according to its final use.
Th~EEU approach is rapidly gaining wide acceptance in the
elect~ic utility industry as the most sensible approach to the
increasingly difficult task of demand forecasting.As mentioned
in our working paper,EEU is a means to combine engineering
infonnation on final electricity usage with economic information
which governs cOhsumer choice.
In an ideal EEU model,not only would basic economic and
demographic variables be modelled and forecasted econometrically,so
too would information on devices which transform electricity
into useful work.The number of"appliances,for example,would
depend on not only the number of households in;a given period,
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21.
but also on the current levels of energy and other prices t incomes,
and even state fiscal policies.
The disadvantage of pure EEU is that it is extremely data-
intensive.This proved most telling for ISER's research;a basic
scarcity of data rendered EEUimpracticable for Alaska at this
time.Consequentl.Yt ISER opted for a llnext best"strategy which
combined an econometric model,MAP,with four new non-econometric
models to produce the required forecast.
3.2 MAP
The Qi'1:.is of the ISER electdcity fo"ec2sting !Tiodel is ~1AP,
a medium-size econometric model of the State of Alaska.HJ\F IS
appropriate for a large role in electricity forecasting because it
was designed to deal with different possible events in the
resource sector and different possible policies for state finance.
Technically,MAP is quite good,as we argued in our earlier
Working Paper.It produces statewide forecasts of employment and
population by age and sex on the basis,of state and national
trends and resource and state government activities.Unfortunately,
MAp·s output is not directly applicable to electricity forecasting
for the Rail bel t and we made .a number of recommendations on
improving this situation,the majority of which have been imple-
22.
mented by ISER in th~ir subsequent work.
3.3 The Household Formation Model
We recommended that the demographic data output of MAP
be expanded to include the number of households by age of head
to complement MAp·spopulation by age and sex.This was carried
out by the addition of the household formation model.
The househol d fOt'mati on model ;s an adequately developed
method of accounting for households but relies only on qemo~
graphic analysis for its aggregation of individuals;no
economic activities modelled in MAP affect household formation.
3.4 The Recional Allocation Model
-~.,.....-~---_....._-;-------
Since t·1AP produces statewide estimates of economic and
demographic variables another required change was to distribute
to Greater Anchorage,Fairbanks.and Glenallen-Valdez appropriate·
shares of statewide activity.The regional allocation model was
developed to meet this requirement.This is extremely important
because resource development projects used in projections of
statewide activity could shift population and economic growth
regionally within the state and even within the Railbelt.
"
23.
The forecasting model must be capable of accommodating
the pass;bil ity of a remote oil di scovery lead;n9 to the ex-
pansion of communities outside the Railbelt grid,for exam~le.
Other scenari os might ;nel ude projects whi ch have a differenti al
impact on the three Railbelt regions.
,
The ISER i approach to this problem is acceptable.It appears
to be a preciSe statistical allocation of regional activity
based on resource sectoj~employmentand other factors.However,
there has been so 1;ttl e vari ation in the.regi anal proportions
of activity in the years for which data is available that the
regional allocation model has not been thoroughly tested.
~hile likely not as DIEc;se as it ~PREars.the regional
allocation model is adequate in the context of the present study;
I
r ;-11 '1~(Jn::de'.''.?1Cr:1'9nt \;,1Oul d be requi red to adeqlJCltely handl e,
unusually-located resource projects or to expand the study area
to other regions of Alaska.
3.5 The Housing Stock Model
The housing stock model is the final bridge between MAP
and the electricity end use model.The most important aspect
of this model is the projecUcm of the rel ati ve proportions
of single family,duplex,apartment,and mobile units.
24.
Like the household formati·on model t the hous i ng stack
model is based only on demographic factors ahd not 00 the
economic output of MAP.Beacuse of the lack of year-by-year
housing data it is not possible to relate housing stock to
construction activity,interest rates,and other influential
variables which would clearly be desirable.
While tile necessary data is missing it is possible to
recreate it in the future on the basis of aerial photography,
utility hookUpst housing sales,and building permit activity.
We strongly recommend this be done in future improvements to
the ISER model.
3.6 The Electricity End Use Model -Residential Sector
The residential part of the end use model applies infonnation
on heating plant,appliance ownership,housing heating efficiency,
and their changes over time to forecasts of households and housing
units.The numerous ways in which this al.lows the analyst to
examine the impact of alternative policy options is admirable;the
detailed calculation process allows for changes in virtually any
aspect of residential electricity consumption patterns.
A major problem in the model is the apparent confusion between
housing stock attrition,which is not in the model but should be,
and heating plant attrition,which is in the model but overly
emphasized.Essentially,the rate of heating plant attrition is
....
"""J
25.
qUite high,given that the heating units of concern are electric
and conseque~tly last indefinitely given repairs to small com-
ponents.
The model shoul~allow for a very slow loss of actual dwellings,
especially mobile homes,and for a somewhat faster loss of heating
plants to newer an~more efficient designs.Consequently,the
particular numerical values used by ISERwhichsimultaneously
understate attrition of buildings and overstate retrofit are open
to question.
3.7 The Electricit.,yEnd Use Nadel -CIG Sector
The commercial sector end use model is quite undeveloped
and spar-se in comparison to the l~esidential ii,ouel.Ot"i~:inol~y,
ISER had intended to build the model on the bas~s of 'floor
space in commercial,industrial,and government buildings with
a very modest breakdown by type of activity.In the final analysis,
employment was used as the benchmark for electricity use projec-
tions.
This is adequate for the present stUdy but is difficult to
interpret as end use analysis as no physical efficiency changes
can bledi rectly related per empl oyeeenergy use.Clearly,a
model based on physical attributes of structures,such as floor
area,would be easier to relate directly to energy use.
26.·
Furthermore,the final results reflect no breakdown of .
commercial-industrial,government into sectors;and breakdowns
published by ISER were generated by across-the-board allocations
of final consumption.
The most important problem in the eIG fdr~cast ~s that the
per-employee energy consumption figures are based on 1973-78
changes in consumption per eIG castomer,i.e.store,factory,etc.
While these two years avoid the highest point in the pipeline
boom which might exaggerate energy use~the'1978 figure may
have been pushed up by the practices of th~boom years (uninsulated
buildings,lights on constantly,etc.)This may be an important
biasing figure when translated forward into per employee values
for future periods.
Alaska's recent energy conservation legislation offers the
strong possibility that a significant number of energy audits
of residential and eIG customers will be carried out ..The audits
offer a prime opportunity to buil d a better data base whi ch
includes information.on the physical characteristics of buildings.
In the mean time,a close scrutiny of actual eIG electricity sales
should offer a check on ISER1s assumptions and should reveal
whether the potential biases suggested above are in operation.
.-:!
27.
Eventually,more detail of the eIG sector must be built into
the model.At the very least,information on principal activ-ity,
size of establishment,and region must be included.As we will
note in the following section.the actual eIG forecast produced
by rSER appears to be based on overlv-rapid increases in energy
use per employee in an era of growing energy awareness.
3.8 Summary
In summary,the ISER method is a major improvement over any
other forecast methods which,to our knowledge.have been used
in Alaska.ltis a two part process with the Stage I model (MAP
plus household formation.regional allocation,and housing stock
extensions)feedinSJ infonnaticr-'0"r'I
and housing stock into an electt"icity end-use modeL The l~tter
an underdeveloped commercial portion.
c o:Y;~)onen t ~.Il-J....../...J.'-'
-----------_._--~,-~-----
28.
4.AN ANALYSIS OF THE ISER MODEL OUTPUT
4.1 Introduction
As indicated above$the ISER model forecasts Railbelt
electricity consumption in terms of energy (or MWh)by end use
and consuming sector$for each of the Railbelt's three divisions,
for the years 1985$1990~1995,...,2010,and for each of
three economic scenarios (which attempt to capture a reasonable
range of economic development possibilities).Of the three
economic scenarios -low$moderate and high economic growth
ISER considers the moderate case to be the "most probable".A
summary of aggregate Railbelt electricity growth for each of
these three scenarios is presented in Figure 4.1 following:
Figure 4.1:Summary of ISER Electricty Proj ect ions ·l
I
Low Moderate Hi gl!.
1985 2921 (GWh)3171 3561
1990 3236 3599 .4282
1995 3976 4601 5789
2000 5101 5730 7192 11
2005 5617 6742 9177
2010 6179 7952 11736 ~.
Annual Growth (%1
1980-1990 3.08 4.18 6.00
1990-2000 4.66 4.76 5.32
2000-2010 1.94 3.33 5.02
Average Annual Growth 1980-2010 (%)
3.22 4.09 5.45
..,.,.
...
collected and analyzed and the model structure improved.
5.3 ISER Model Automation--_._--_.--------
While the ISER MAP model is fully automated,the end use
model at present consists of several hundred worksheets,
changes to which must be made manually.In this form,the
end use model is virtually inaccessible to analysts who might
wish to test the effects of various end use assumptions:the
development of a single alternative scenario for the entire.
Railbellt would take many days.This serves to .limit the
potent-ial of the model as a policy analysis tool.
Ideally,the entire forecast model,that is,the MAP,
hOllc:ehnlcl fOlo'ni'!tion,housing.)"egio'lal allocation and end lise
components,\'Iould be automated.·WE:believe that such an
effort should be made.
5.4 Future Use of the ISER Forecast....--"--~------""""-~'_.._---~--..'_."""
Because the ISER model represents such an advance over
previous forecast methdds,we bel ieve that it should be
utilized in the evaluation of future energy projects in the
Sta te.In ot her word s,whil e spec i fic assumpt ions can,
of course,vary over time and among analysts,they should
be incorporated,and the results viewed,within the context
of the ISER forecast.Efforts should be made to improve
the weak points of the forecast,the result of which would
45.
46.
be a forecast structure which forms an excellent basis for
project evaluation and policy analysis.
~.5 Data Collection----------
Data collection methods within the State should be
improved,in at least the following ways:
(a)the results of the 1980 Census should be incorporated
into the forecast at the earli est opportunity;
(b)air photo interpretation should be employed to
reconstruct the building stock for the Railbelt;
(c)information from the energy auditing programs should
be used to 9a i,n a full er understandi ng of the CIG and
residential building stocks.
Data should be collected,and the ISER model revised and
expanded,so that the model can ,be used to forecast electricity
requirements for the ent'ireState of Alaska.This will
require several structural revisions to the model,especially
with respect to the regional all Dcation component.
5.7 Peak Demand Forecast;ng
A peak demand forecasting method should be developed to be
applicable to all Stage I and Stage II scenarios.This analysis
h ~_
should be conducted by estimating and summing the load
characteristics of each individual end use.The potential
for load management and the effects of time-of-day pricing
should be considered in the research.However.at the
present time.we do not bel feve that it would be worthwhile
to develop an 'integrated energy/demand forecast.
5.8 Additional Stage I Scenario.-.....
,At present.all three Stage I scenarios developed by
ISER ,assume a steadily increasing level of State economic
activity.However,the possibility of a significant
slowdnwn in resource sector activity during the 1990's has,
been considered by a number of individuals.resulting from
the depletion of the most Rccessible and least expensive
natural gas and oil deposits.Given the real possibility
and significant consequences of such a scenario,we believe
that it would be worthwhile to model this possibility in
the same fashion as ISER has modelled the three major
scenarios to date.
5.9 Independent ExeertAdv;ce on the Load Forecast
It has been argued that an appropriate way to review and
evaluate the ISER model results would be to draw together a
groupl of individuals familiar with State economic and energy
affairs.This group would evaluate the likelihood and
feasibility of the model's assumptions,from which a.fuller
47.
appreciation of the range of possible e,lectrical futures
could be obtained.
We believe that such an exercise might prove fruitful
for two r~asons.Firstly,such a group might achieve a
CDnsensus with respect to p~obable electrical futures (or.
fail ing consensus,might better understand the assumptions
about which the group cannot agree).Secondly,the logic
behind the ISER method could be spread over a wider range of
parties.resulting in a deeper appreciation of the factors.
affecting electricity growth and the role of State policy
in these areas.
We should qualify the above,however,by stating that
48.
policy intervention can assist in detenrJining the "probability"
of a parttcular electrical future;thus this approach should
be seen not as a substitute for.but rather as a complement
to.continued energy policy research in the State.
A-I
APPENDIX
WORKING PAPER #1:A PRELIMINARY EVALUATION OF THE ISER ELECTRICITY
DEMAND FORECAS-l
..J.a1!!!.c'!.!:Y...2,1980 (amended for inclusion)
Preface
In October 1979,Energy Probe was asked by The House Power Alter-
natives Study Committee (HPASC)of The Alaska State Legislature
to submit a proposal for a study that would evaluate the elect-
ricity demand forecasting method developed by The University of
Alaska's Institute of Social and Economic Research (ISER).
This report presents an initial evaluation of the ISER forecasting
model and the Man in the Arctic (MAP)model on which,in part,
the electricity demand forecast is based.
The'present report draws on information contained within the
Detailed Work Plan submitted November 14,1979,by Dr.Scott
Goldsmith of ISER;May 1979 MAP model docu~entation;various
publications relevant to the future social and economic activity
in the State of Alaska;and personal discussions ,with ISER
personnel.
A further report will deal with the sensitivity of electricity
growth in the Railbelt region of Alaska to policy and market
induced changes in the social.economic and physical factors
which influence electricity growth;and with an analysis ·of the
appropriate tole of electricity demand forecasts rvHhin the [;1'uctGET
context of State energy policy development.
8ecaus€!this report is a working document intended only for use
b,j'f,;PASC melnbers and consultants,it is \-witten '~n ~'elativE:Jy
technical language.Our final report will detail the three
areas mentioned above in less technical terms.
The views expressed herein are those of the authors,and not
necessarily The House Power Alternatives Study Committee.
1.Intr'oduction
Electricity demand forecasting,like all quantitative forecasting,
is an E~ffort to view the past and present in a systematic way with
a view towards making reasonable statements about the future.
The ba!iic problem is that the.future is not known,and indeed can-
not be known,even in a probabilistic sense.'As a matter of
fact,pretending to forecast the future is an indictable offence
under the New York State Criminal Code.(1).Similar provisions,
we are cerdin,are 1n effect elsewhere.'
Howevel·,analysts often find it necessary to fly in the face of
strict legality when the Viability of a large project hinges on
A-2
the need for it in the future.Hence,forecasting has become an
integral part of planning for investments in energy,transportation,
housing,and a myriad of other functional service delivery areas.
Forecasting the demand for such services comprises a two stage
process.In the first stage,aggregate social and economic
activity is projected into the future (using,for example,the
lSER MAP model);the second stage translates this aggregate
activity into a detailed forecast of the demand for the produ'ct
or service in question.
Stage 1 models tend to be rather ubiquitous,finding application
in a number of functional areas.MAP,for example,has been
used in a variety of forecasting environments including energy
impact analysis and fiscal forecasting.Gnthe other hand,Stage
II models are generally specialized and tailored t6 the.problem
at hand.In transportation planning,they are classifi·ed under
the general headi ng of travel demand model s.In energy demand
forecasting,a number of different approaches have been developed,
which have met with varying degrees of success.To the extent
that a debate over appropriate forecasting methods exists,it is
rea11y a debate over the choice of a Stage II approach.In fact
as we argue below,the choice of a Stage II approach essentially
dictates the output and hence the structure of the Stage I model
to be used..
The argument over Stage II models centers on the extent to which
the model should deal with two distinct but equally important
aspects of the probl em.Given an aggregate forecast from Stage
I,should ~Stage II model focus on the specific activity
"invo'lved or shollld it focus on the decision of the cons')rnina
unit?:In forecasting I'/ithin a polic:y envir"ohment concerned'l'lit!:
housing,for example,the latter dictates that we ~xamine housel;olJ
budgets,prices and so on.However,a dwelling offers service
far beyond simp"l e shelter;amenity,proximity and opportunities
for social intpraction are but a few of these.Hence,the former
approach would.argue that the demand for housing ;s really a
composite demand for the services offered by a structure.Energy
and transportation are similar.Rarely are they required for
their own sake:in reality they ar~crucial inputs into a number
of satisfaction-yielding activities.
In el ectric Hy demand forecasti ng it was once possi bl e to do a
reasonabl e job of prediction by looking at a historical growth
rate and simply plotting future levels of consumption against
time.A draftsman with a French curve (or an engineer with semi-
log paper)could make a reasonable guess at future demand by'
simple curve fitting and extrapolation.However,it is logically
clear that the growth in electricity demand has little to do with
the passa,~of time ear~.Rather,it is related to individual
.9E!Cl:§ions--to engage in a growing number of el ectricity-using
activities over time.
2.Stage II Modelling Approaches
Attempts to deal seriously with this complexity became necessary
in the early years of the 1970's when historical rates of elec-
A-3
tricity growth ceased to be real ized by most el ectrical util Hies
in North America.The formation of OPEC and the 1973 Arab oil
embargo,with its subsequent increases ;n petrol eum prices.ended
the era of cheap energy;and all fuels.including electricity,
rose in price rather dramatically.Unfortunat~y,the econometric
demand forecasting models in use at this time (2)were incapable
of dealing with such rapid changes and continued to point to
historic or near-historic rates of electricity growth.ISER1s
1975 electricity demand forecast for the State of Alaska (with
which,we might add,lSER itself was not comfortable)is a case
in point.The most telling criticism of its strict time-series
econometric approach is that potentially ludicrous a<;tivity
forecasts result.In ISERls 1975 effort,for example,initial
results indicated a demand for el ectricitywhich impl ied 100%
saturation of electric space heating in Fairbanks in the future.
The point to be made here is that because individual activity levels
are not explicitly identified in aggregate economic mddels,such
models run the risk of implying physically unrealistic activity
1evel s.
End uie forecasting models in their pure form take the opposite
approach by Y'ely'ing almost exclusively on activitie~,independent
of the underlying economic conditions.The logic is simple:
consuming units engage in various activities requiring energy.
Energy glrowth can result from
(a)
(b)
(c )
:d)
engaging in additional energy consuming
activities;
engaging in the same activities more intensively;
eng a gin gin 't he S cUi I e i:i c·t i v i t -;e s ~s ~~S L T 'j""j ,~.~c::.~~:.\",~
any cOillbination of the above.
"".
The case of oral hygiene provides a humorous example.,A household
may switch from lImanua"j"to electric toothbrushing,an additional
energy using actitity.Given an electric toothbrush,members of
the household may wish to brush more regularly.When the tooth-
brush wears out it maybe replaced with a model which delivers
fewer brush strokes per unit of energy input.In any of these
.cases,electricity use increases.In principle,it is possible to
examine all electricity use in this manner,noting that all energy
is used iin a final form such as heat,light,motion or sound,and
that it is transformed from its input·form to its final end use
•form by cl I!device"..
A9ain,in principle,electricity demand can be projected by fo,re-
casting the characteristics of devices and activities.This
has become known as the engineering or end use approach to demand
forecasting.The most tell ing criticism of this method·in its
pure form is that it is not sensitive to changes in prices,incomes
and prefi!!rences,i.e.the decision aspect of the process modelled
in Stage II.This is a generally accurate criticism whose resol-
ution re~luires an examination of pol iciesaffecting the decisions
of the ir1ldhidual consuming unit ..In further work for HPASC,we
will be dliscussing this problem.
A-4
For functional forecasting purposes,an approach is emerging which
seeks to overcome the inherent difficulties of both extremes of
Stage II modelling methods.The econometric-end use approach
(EEU)attempts to deal with el;ectricity use at the level of the
E~J:j_vity while recognizing that the decision to own and operate
a device,i.e.to engage in an activity at some level of intensity,
is inherently a problem of microeconomic choice and is therefore
sensitive to prices,incomes and the availability of alternatives (3).
)~our:._~pi rii C?!ll __an_JIlL.approach is the only setls i bl eway to forecast
.eJ.~~_~!icit.i clem_and and to justify a huge e1-penditure of public funds.
We are pleased that ISER agrees in principle with this general
philosophy.The detailed work schedule circulated by ISER lays
out a rather impressive work plan.We anticipate problems arising
becaUse of the extensive data requirements of EEU,which will be
intensified by the basic data problems of Alaska:short time series
and small population.However.we fully support ISER's desire to
cast the net widely at first.while recogniZing that data,and
more importantly time and financial constraints will require the
net to be drawn in.somewhat.
At this point we would like to comment on the allocation of resources
for independent demand forecasting relative to the magnitude of
potential capital investments.Given the magnitude of the stakes
for a project such as $usitna,i.e.a potential investment of
billions of dollars,we feel that far too little money is being
spent on this crucial element of project feasibility.ISERwill
likely argue.and justifiably so,that data is simply not availa.ble
1.0 construct a full scale EEU model.The missing data,hmvevel",
is not of the variety which is impossible to collect.With
.additiona1 resources made available,it could be gathered and
'incorporated into the forecast model.resulting in a forecast
method with which all could be reasonably comfortable.
In the following pages we will review the EEU approach to Stage II
and the requirements of a Stage I model to provide requisite
inputs into EEU.Our goal is twofold:first to analyze and suggest
approaches to particular problems for the benefit of ISER,and
secondly to layout the logic of ISER's forecasting proposal for
the benefit of all cons.ultants involved in HPASC studies.It is
our hope that this will facilitate discussion and understanding of
ISER's methods and in the longer term,identify avenues for
potential policy intervention..
L.The Econometric-End Use Approach
EEU beg"ins with the simpl e proposition that all energy is used in
capital items or devices,which perform a specific task,.i .e.an
end use.Each device.by virtue of its de~ign,has a specific
energy input requirement for each unit of useful output,a concept
similar to ilFirst.law ~fficiency".Devices are owned or rented
and operated by consuming units.However,not all consuming units
own all types of devices,nor do devices operate at all times.
>,..
A-5
Further,many devices may be powered by more than one fuel.The
decisions to own or lease and operate a device are economic
decisions made by the consuming unit in light of prices.incomes,
preferences and available options.Fora given period,say a
year,the total energy required by a consuming unit to power a
given device is by definition its hours of operation times its
power requirement.If the device is electrically powered,this
energy dE~ma nd will cantri bute to an e1 ectric ity demand est ima te.
Any port'ion of the e1 ectric power consumed by the economic unit
which it generates itself,does not contribute to this utility
forecast"
There are,of course,many consuming units and many devices.We
may translate from the device level at the consuming unit by
simply summing over devices and consuming units yielding the
following expression for utility electric demand over a period of
one year:
N M
TUD =~~
k=1 j =1
(1)
where
TUD
Dkj
Ekj
lkj
HI,;
.t,,)
Sk
N
r~
=total uti'lit.y demand (kW.h)
::1 (if consuming unit k has device j)
a (if otherwise)
::1 (if device j is powered by electricity in consuming unit k)
O(1f otherwise).
=intensity of use of device j by consumihg unit k (hours)
pO\:el~f'ElWll'ement of devi·ce j by consurninCi unit k (Hi)
~amount of self supplied electricity by consuming unit k (kW.h)
::total number of consuming units
::nUl'nher of"di stinctdevices
(2 )
M
".E.(N.x PD,..x PE..x I..x R..-S.)j=1 1 J lJ lJ .lJ 1
This ;s an account"ing framework for utility demand (4).To
operationalizeit for forecasting purposes,each of the components
must be related to known of "knowable"variables.Engineering
.knowledge and economic theory suggest potenti~l relationships.
Econometdc or other techniques are used to estimate their direction
and strength.
For operi:ltional purposes it is necessary to group consuming units
into classes on the basis of predominant activity within the unit
(i.e.residential,commercial,etc.),similarity in patterns of
device o'fJnership or energy requirements,or some other appropriate
criterioill.Clearly,there are losses in precision due to this sort
of aggregatioh.After grouping consumingunfts into classes,the
demand for utility electricity is obtained by the following
expressil:>n:
TUD ::l CUD i ::~
i =1 1=1
,}
.-.
-_._.._-----_.._._--_._-------------------""'-
where
CUD.1
N;
,PD •.lJ
PE ij
I ..lJ
R•.lJ
5 i
Q
==the demand for electricity by class i (kWh)
the number of consuming units in class i '
==the proportion of class i consumers owning device j
==the proportion of device j in class i that are
electrically powered
==the average intensity of use of device j by members of
class i (hours)
=the average power requirement of device j owned by
members of class i (kW)
==the amount of electdcity self supplied by class i
members (kWh)
==the number of consuming classes
A-6
..~
~.
The advantage,of exarn'ining end usedemand in this manner.is
obvious.Not only is it less data intensive than Equation (1).
but also,key parameters become easier to pinpoint.For example,
in an analysis of a subclass comprised of mobile homes built
before 1970,space heating requirements would be rather similar.
Time,of course,is also a crucial consideration which must enter
the model in a forecasting environment.The ~dvantage of an
end use model is that the factors developed above exhaust the
realm of demand factors,and each will change ,over time.As
time passes,classes of consuming units grow or dec11ne,devices
become more or 1 ess preva 1ent and more or 1ess "el ectrical",
se1f~supp"lied electricity may become more widely llsed,devices
may be used more or less intensively,and device efficiencies
will undoubtedly change.The latter is particularly important
since many devices will be replaced over the forecast period
and those which are not may be "retrofitted"to improve their
rerforrna nc e.
Whil e the passage of time is itsel f not the reason for'change,
the argument above suggests that it may prove fruitful to "
view demand grovlth in a temporal sense.At a point in time we
begin \'lith a "stock"of consuming units equipped with devices.
Over the ensuing year the consuming unit may disappear,change
or modify its collection of dev"icesormeans of powering them.
In addition,new consuming units may be formed complete with
new devices.Presumably these new devices would have energy
consumption characteristics d'ifferent from "old"devices.At
the end of the year we witness a revised stock of existing
consuming units and devices comprised of the previous year's
units plus net increas~s;This may be taken a year at a time'
over the entire forecast period yielding electricity require-
ments for specific annual points and annual incranents in danand.
4.The ISER Model and Suggested Approaches and Revisions
In the context of the Rail belt region,EEU makes a great deal of
~;
A-7
sense for the residential and commercial sectors which,taken
together,account for about 86%of Alaska IS total electricity
demand.Because industrial development in Alaska is largely
of the major project variety,it is best to examine these in a
case by case manner.!Further,with the exception of block
heating in vehicles,the transportation sector currently uses
an insignificant amount of electricity.Again,this is best
viewed as a special case.'
ISER1s EEU model,Figure lin their IIDetailed Work Plan 'i ,
incorporates most of the features of an ideal EEU discussed
above.It is a stock/flow model which segregates consuming
units into II new "and lI o ld ll and deals with four residential
subclclsses.and segregates devices into six categories incl uding
an "o ther ll category for minor appl iances.
The commercial sector should be divided into at 1 east the
following groups:
(a)public/institutional buildings;
(b)large shopping plazas/office buildings (say larger
than 100,000 or 250,000 square feet);
(c)other commercial buildings.
This would be fruitful for two re~sons~within each group there
are s'imilar requirements for electricity;and policies/programs
may bl~specifically tailored,at a later date,to this partic-
ular pattern of consumption and occupancy/ownership.
Missing in ISER's proposed model is a term to account for
el ectric ity or energy su ppl i ed by the consumi 119 unit and hence
not required from a central system.This should be added to
tile illudel even thDugh it Iilay liiJt greatly affectbhe rhagniLLAuE::of
the final fOI'ecast./\number'of considerations vlarrant it:;
inclusion,not the least of which is the possibility of
co-generation of electricity and steam for space heating in large
commercial establishments,schools,hospitals and the like.
The present ISER formulation allows for the scrapping of dwelling
units but not for the replacement of appliances within
existing units.A number of appliances ISER intend£to consider
have useful lives of substantially fewer years than either the
forecast period or the structure.In ISER's model,this problem
could be solv~d by adjust-j ng the average consumption of appl iances
on an annual basis.It is better,however,not to confound the
effidency measure with the effectof new appliance stocks.
Given these structural refinements which we consider necessary,
the ISER approach to residential and commercial electricity
demand forecasting is methodologically sound.Since residential
and commercial consumption inthe Rail bel tis.quite important,
it is necessary to exami ne the components of the EEU mode'and
to suggest possible approaches to modelling each component.In
this case we refer initially to our formulation of EEU above,
and el<pl icitly to these el ements pertaining to Stage II.
In Equation (2),total utility demand was expressed as the sum
of class demands •.Class demand is a function of the number of
""-,_._--,--------_._._-"---~.._---------------~------------~,
A-B
units in the class,the proportion owning various devices,the
proportion of these devices powered by electricity,the average
intensity of each device's use,the average power requirements
of the various devices and the amount of self supplied electricity.
The number of consuming units in eath class is essentially a
modified form of the output of State I which we discuss below.
The remai.ning factors are,however,Stage II concerns which we
deal within turn.
POij.the proportion of class i units owning device j,is
obviously a variable whose value lies between 0 and 1.For
certain end uses,i.e.space heating,its value equals unity and
will continue to do so over the forecast period.In other cases
like clothes drying and refrigeration.its value is a matter of
choice.and while perhaps initially close to unity,it is variable
over the forecast period.In an ideal world we would hope to
estimate this proportion on the basis of income 1evel and
distribution within the Railbe1t region,bearing in mind that
the decision to ovm a device also commits the owner to operating
expenses over its lifetime.Hence the general price level of
all competing fuels may be important.
PEij,the proportion of device j owned by class i which are
electrically powel'ed is also a variable whose value ranges from
o to 1.Again,for certain end uses,especially refrigeration,
its value is close to unity and will likely remain so over the
forecast period.However,a great deal of choice exists in this
area.A useful way to look at this prob1 em has been proposed by
Fuss who suggests the decision to engage in an activity with a
specific fuel is essentially separable.In other words.given a
decision to ellS!21~12 in an activity,the choice G{fuel is essen-.
tiallya separate question (5)made on the basis of relative
prices,
The question of the treatment of conservation arises in this
instance.If cnns~rvation is factored into average energy
requirel1lents,then no more need be said,However,if we view
each or any device as having a '1base-line lt energy requirement,
then any effort to reduce it involves an explicit tradeoff of
electricity for conservation.In this sense.conservation is
self-supply,and has an average supply price equal to the·
amortized a.nnua1 cost of the conservation project divided by the
number of kilowatt-hours displaced during a year.Marginal costs
may be calculated by assuming.ideally,various levels of conser-
vat ion and cal cul at i ng.presumably,a step function for the fuel
equivalent value of various conservation schemes.The same
logic may be applied to renewable energy projects as well.
We feel it is usefUl to view conservation and renewables in this
\>Jay when considering existing activities at a point in time.The
major point is that given an existing activity,like space and
water heating (the major ones)the consuming unit can choose not
only to switch from one conventional fuel to another but can also
choose to supply a portion of its requirements with conservation.
In an oil heated home,for example,the household may switch to
gas,electricity,or conservatiDn for all or part of its heating
f''.
A-9
on the basis of relative prices.Considering conservation as an
explicit fuel represents a useful modification of interfuel
substitution analysis.
Rij,the average power requirement of device j in class i,becomes
basically an engineering design parameter when conservation is
treated as a fuel.Consequently,it is a function mainly of
vintage,not confounded by retrofit.One item that should be
examined is the trend in device efficiencies over time.This
may well be an appropriate area for regulation.
Iij,the average intensity of use of device j by class i members
is also a consumer choice variable although to a limited extent
in the major consumption categories.Actions like reducing
inside temperatures and the like are evidence of the ecoilomizing
behaviour of households under this category;how much further we
can go in this area is certainly questionable.In this case,
comfort and convenience bound choice from below.To the extent
that there is flexibility it is likely price and income related.
The final term in our formulation 1s 51,the amount of self-
su"ppl ied electricity by members of class i.In this instance
we suggest that this term be kept pure in the sense that conser-
vation not be viewed as self supply in this term.We include 5i
in the model for the reasons stated above.There is a price at
which self generation or co-generation becomes attractive whether
by means of water power,wind or conventional fuel.The model
should be sensitive to this possibility.
The above relates to our formulation and al so to ISER 1 s model.
The remaining terms in ISER's model relate to new household
:fc5Y'iIJdt'ioll \'.ll)t.i -~'12 d'jscu:__,~,:_O;l.:.::;.i:J tIle \,'-&(-;,~.l'~'C''''(lPi)~ng
JcJ1.es".SCI';.I))il'19 of a uE'c"idim!o'ives riot CJr:y phys'ica'!
deterioration but also economic considerations,one of which is
the device's fuel requirements.Logically,the scrapping rate
should increase with decreasing energy requirements for ilew
;;,oaels of d ('tkular device.lids is extracrc.iinarily diff'ic0.!t.
to measure and project over time;however,it is something to
be ke pt in ill i nd .
Generally speaking,we are impressed with ISER's proposed method
for handling the Stage II modelling of the residential and
commercial sectors.With the modifications suggested above we
can who]eheartedly endorse ISER I sapproach and we look forward
to working with ISER on!further questions of approach and
sensitivity analysis.With respect to the ISER approach to
non-r'esidential and commercial use of e1ectricity,we reserve
judgEment si nee the method has not yet been developed.We will,
of C(llurse,comment at an appropriate time and we are confident
that ISER will take a sound approach t based on their work to date.
5.Stage I AEproaches
We nCit turn to the merits of the MAP model of the Alaskan economy
as a Stage I model for EEU forecasting.Regional economic
forecasti ng can take a number of fonns.Some approaches bei ng
number
That
number
A-IO
considered in the "Detailed Work Plan ll are input-output analysis,
the economic base approach,Curtis Harris l locationally efficient
model,and the Delphi technique.These all have strong and weak
points but none is a serious contendor to a moderately detail ed
econometric mode'l 1i ke MAP..
What is required of the Stage I model?It must provide the
of consuming units in each class for the end use equation.
is,in the numbel~of housing units of severa.ltypes and the
of firms,'employees,square footage or bus i nes's volume for
commercial and institutional units.It must be sensitive to the
scenarios of fast.likely and slow growth mentioned in the
"Detailed \4ork Plan".It must respond to changes in oil and gas
pricing,energy and other major investment projects,national
economic trends.and demographic realities including migration.
While the current MAP model incorporates most of the latter
functions,the restriction of demographic projections to persons
(not households or families),the introduction of housing only
through the dollar volume of construction~and the lack of other
physical measures of economic activity closely related to the
number and type of consuming units are major deficiencies.As
noted in the "Detailed Hork Pian",data must be gathered and
incorporated into new versions of MAP~.
What regional techniques must be added?In our~pinion,none of
the above mentioned techniques merit much effort.
Input-output analysis is appropriate when a region has a large
industrial base wh"ich t'el ies to a great extent on inter-industry
sales.Alaska does not have such an economy yet,and the methodts
,·/("11 known data intensity sliggests that it nepd not be considered
further',Shortcuts to true regional input-output data gatherirH}-
such as the use of technical co~fficients borrowed from other
studies -are inarrwopriate for an unusual state E?conomy such as
that of Alaska..
Ttl",~,tT()liSl point~~of ,,~COnOd!jc base analysis -atechnique','ih·ich
is useful when the regional economy pivots on clearly defined
basic industries -are already contained within the MAP mode1.
The simple economic base methods are too elementary;ISER is
well beyond them already in its work.The same criticism holds
for purely extrapolative methods.Just as ruler~and graph
paper are inappropriate for load forecasting,they are too
simplistic for the economic part of econometric-end use analysis.
Curtis C.Harris developed a regional forecasting mod~l at the
detailed industry level based on short time series changes in
output by industry and state and incorporating tr.ansportation
costs estimated by optimization techniques.Alaska clearly is
not likely to exhibit consistent locational cost patterns of
industrial development necessary to take Harris I approach.
Delphi,a technological and political forecasting technique
.developed first at the Rand Corporation is unlikely to yield the
moderately detailed consuming unit forecasts needed here.However,
it may always be cons"ldered for developing scenarios for energy
projects,general economic growth levels.or energy policy
A-ll
decisions.Hence it is not a Stage I model but a source of
exogenous and policy variable values for any forecasting method.,
Among general methods for forecasting regional economic activity,
one not yet mentioned is shift-share analysis.This method is
basE~d on statistical estimation of the contribution to d state's
industrial growth of industry factors and regional factors.It
is an excellent basic method which is sufficiently incorporated
in a MAP-style econometric approach.While both input-output and,
shift-share methods are usually performed with a great deal of
industry det.ail.such detail is not needed in our Stage I approach.
What is needed is more detail aimed at household characteristics
and bu ildi ng__?toc k c haracteri st i cs.Whlreda-~sourc end poi nts
for households are well known and trusted,a region such as
Alaska can have rapid and crucial post-Censal fluctuations in
households and household size.As for buildings,only dwelling
units are enumerated in the Census.Building stock estimates
for non-residential units are rare above the city leve'J (6).
Land use surveys and Civil Defense surveys give spotty data sets,
but the building stock is basically an unknown quantity for regions
stich as states.For the current research;increased information
ant he bUilding s tacit is important.
As an expedient is is suggested that housing be looked at in
detail (so as to allow better end use forecasts for space and
water heating,lighting and appliance loads);that large commercial
and institutional uses be examined through enumeration of
structures;and that the rest be treated by the use of ernployment
or sales estimates.
:-".,
!,,-;;.:.:..~<'~",.:.-,~-).'-'
~)PI'"'OdC hc~::
,(a)macroeconomic econometric models such as MAP;
(b)Iil'icrvecuJlolidc simulat.i(Jiis of COfLSUi.t)!lj Jilit lesVJi,ses
to changes in price,income and the availability of
The former is necessary to introduce national and major regional
trends.The latter is used to discover what the distributional
effects of new pricing and supply levels will be.
A study commi ss i oned by a number of New York consumer groups and
carded out at Cornell University was used in testimony before
the New Yor k State Energy Master Pl an t4eet i ngs in September 1979
(7).In this approach,Green,Mount and Saltzman utilized a
four-sector economic/demographic state econometric model with a
part'ia11y integrated energy sub-model.The four sectors were
residential.industrial.commercial'and transportation.An
major energy types-electricity,oil,gas and coal -were
forecasted simultaneously.Thi s Cornell model as well as another
mod ell developed with end use detail by The New York State Energy
Office.predicted significantly lower electricity requirements
than has previous state plans.It should be noted that while the'
Cornell model is not extremely complicated (57 economic equations.
150 demographic equations)it contains household formation
functions for each age~sex cohort.Unfortunately,the Cornell
model does not give explicit place in its structure to self-supply
wood space heating or conservation..
A-12
Furthermore,in the Cornell approach,a microeconomic simulation
was linked to the macro model in order to relate income and price
changes and restrictions on fuel ,supply to consumer demand Tor
the different fuels (8).This,of ,course,requires an extensive
data'base of individual households studied by survey research
methods.In this case a sample of 7000 households was utilized.
While such microsimulation may be beyond current possibilities
in evaluating Susitna (and we are not convinced that such further
study should be considered extravagent)it suggests again the
need to make the energy forecasting version of MAP more oriented
to consuming units,households,and the biggest devices of all,p.I:l_LLd i ~_92..--------
Looking in more detail at MAP,based on the may 31 1979 document-
ation,we note that it has more than enough economic detail,but
not enough demographic information because of households not
appearing explicitly.Finally,a housing and/or buildings
component is lacking;this is a critical shortcoming."",
In the fl'Detailed Work Plan",\'ie support most strongly Items A7-9
on,electricity consumption;Item 10 on households,houses and
appl iances.These are more important,in our estimation,than
the refinement of the MAP economic model ~,?e.They should
receive top prior'ity.
Regional disaggregation (Task B)is important,but less so than
getting on toEEU forecasting for the Railbelt region as a '
whole.Thus the items in "0"arecrucial -interfuel substitution
.EJ,.~~the addition of conservation.
,(I,OPT1H'Cil '''''l1::2tion of the r'1Ml mcioelsserves to reveal s'everal-'-.".
strengths in ;:cidition to the <Jhove shDr,tcomings.First,despite
the limited length of the Alaska data series.the resulting'
equations are adequate by conventional,statistical benchmarks,
at least for forecasting use.The detailed fiscal ~nd nati~e/
non'-nativi'/military results,needed for earlier appliCi'lti:o,ns,are
well developed,but may not be particularly helpful in the current
application.
What is needed,more than any other modification,is a housing
sub-model.Whether the data can be gathered for such an ~ddition
remains to be seen.Lacking a formal housing model.some inter-
mediate step is reqUired based on the housing stock data from the
decennial censuses.A brief outline of each alternative is in
order.
A full-blown econometric sub model for housing would flow from the
folloWing modifications to MAP:
(a)inclusion of household formation equations in the
demographic sub-model;
(b)a set of equations for the housing stock (or alternatively
changes to that stock)by age and type of unit.
Some of the ~rucial right hand variables would be from the
construction and investment functions of the economic model as well
as the household formation results.
...
A-13
If the time series data are lacking for the housing modifications
to MAP,then the available census benchmarks -number of dw~lling
units by age and type -should be combined with recent data on
housing starts,mobile home sales,building permits,etc.,to
updatE~the distribution of the housing stock.This results in
the following structure:
Stage I Stage II
_~A ....A
r '1 f'------................._-
MAP---~eco nom ic demogra phy .----------..;)0-....
including households
I....__-::i;;....match of
I
-'!!~,..hou se ho 1ds to --~--~.,...,..
houses
housing stock estimate of
data-..~future stock
'"building data
6.Conclusions
Energy demand forecasting,the most crucial element of energy
p(]lie y d C'v c 1C j";C !it ,i s difficult in the -;::[c C of 9r 0 "r;n9 .
uncertainties.In order to maintain confidence ,infore::.asting
procedures,the analyst is faced with the need to d~velop what
amount to relatively more sophisticated models and forecasts
than has traditionally been the case.
Pure eCUIlOilletric and pure end use forecasts suffer inadequacies;
hence,a blended approach combining the best elements of each
is necessary.This blended EEU approach is difficult because
of·its datarequiranents and because modifications must be made
to the structure of the underlying econometric and end use
models on which it is based.
In the:long run,an fEU forecasting system for Alaska can be
developed with MAP,suitably modified,at its heart.Its data
requirements are not yet attainable in a small region such as
Alaska with a short data history.Therefore,in the short term,
ad hoc forecasting must be carried out with the outputs of the
current version of MAP.These outputs must be obtained by using
a very wide range of input sce.narios.
The most crucial shortcoming of the current version of MAP is the
lack of a housing sector and this must be bridged by some reason-
abl e.if imperfect method of estimating Al askan ·housing stock and
characteristics in recent years.
A-14
7.Footnotes
1.Joan·Robinson,"What are the Questions?",Journal of Economic
Literature 15,December 1977,p.1322.
2.fiiese-are-extremely expensive and sophisticated versions of
semi-log paper.See Herman Daly,lIEnergy Demand Forecasting:
Prediction or Planningt'•Journal of The American Institute
of Planners,January 1976.
3."RobertW:Shaw Jr.,"New Factors in Utility Load Forecasting",
E.~RlL~__~_U_~U.!ies fortnighfu,July 19,1979,pp.19 -23.
4.Much as Dr.Goldsmith1s is a stock/flow approach to accounting
for demand.
5.M.A.Fuss,liThe Demand for Energy in Canadian Manufacturing:
An Example of the Estimation of Production Structures with
Many Inputs",Journal of Econometrics 5,January 1977,pp.
89 -116.
6.B.Jones,D.Manson,J.Mulford,M.Chain,The Estimation
of BuJldjJ.19 Stocks and their Characteristics in Urban Areas,
Program in Urban and Regional Studies,Cornell University,
1976.
7.·W.Greene,T.Mount,and S.Saltzman,"Forecast of the Demand
for Major Fuels in New York State 1980 -1994",Technical
Report,September 4,1979.
8.S.Caldwell,W.Greene,T.Mount and S.Saltzman,IIForecasting
Regional Energy Demand with Linked Macro/Micro Models",
~orking Paper in Planning #1,Department of City and Regional
Planning,Cornell University,January 1979,forthcoming in
Pa£ers of the Regiona1 Science Association 45.
./..
...