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8/2/2019 Energy Demand Forecasts
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ENERGY DEMAND FORECASTING METHOD XA9745302
BASED ON INTERNATIONAL STATISTICAL DATA
Z . GLANC, A. KERNEREnergy Information Centre,
Warsaw, Poland
Abstract
Poland is in a transition phase from a centrally planned to a market economy; data
collected under former economic conditions do not reflect a market economy. Final energy
demand forecasts are based on the assumption that the economic transformation in Poland will
gradually lead the Polish economy, technologies and modes of energy use, to the same
conditions as mature market econ omy countries. The starting point has a significant influence
on the future en ergy demand and supply structure: final energy consum ption per cap ita in 1992
was almost half the average of OECD countries; energy intensity, based on Purchasing Power
Parities (PPP) and referred to G DP , is more than 3 times higher in Poland. A meth od of final
energy demand forecasting based on regression analysis is described in this paper. The input
data are: output of macroeconomic and population growth forecast; time series 1970-1992 ofOECD countries concerning both macroeconomic characteristics and energy consumption; and
energy balance of Poland for the base year of the forecast horizon.
1. Background
A typical energy demand forecasting procedure [1] assumes the following steps:
1. Estimation of the base year useful energy demand as a product of base year energy
consumption and conversion device efficiency .
2. Assessment of the improvements in conversion device efficiency over the planning
period.
3. Estimation of changes in the useful energy requirements in future years.
4. Determination of the relationship between macroeconomic cha racteristics and energy
consumption growth for each demand category.
5. Determination of the future useful energy demand as a product of the base year
useful energy demand, total improvement of conversion device efficiency, and
economic growth parameter.
Concerning the data normally used for forecasts, it is necessary to point out that:It is inappropriate to use P olish statistical data to describe historical ma croeco nom ic
behaviours and energy use/production relations. The country is in a transition phase
from a centrally planned to a market economy: data collected under former
economic conditions do not reflect a market economy.
Reliable end-use energy demand data are not available since the first surveys
concerning energy use in households and public sectors were conducted in 1994.
The only available model for macroeconomic growth forecasting is the SDM-NE
(Simulation Dynam ic Model of National Economy) model. The output of the model
for every branch (global output, value added, investments, etc.) is expressed in
monetary terms.
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A typical energy demand forecasting procedure cannot be applied properly, in Poland,because of:
the unreliability of the relationship between macroeconomic conditions and energy
consumption growth;
the insufficient knowledge about conversion device efficiency;
the lack of information concerning improvements in device efficiency and altering
of useful demand requirements in future years.
2. Approach
Final energy demand forecasts are based on the assumption that the economic
transformation in Poland will gradually lead the Polish economy, technologies and modes of
energy use , near to the conditions of m ature marke t economy coun tries.
After the economic "shock therapy" of 1989 and early 1990, which brought in a severe
contraction of economic activity, the econom y stabilised in 1992 and 1993. Energy and
electricity prices increased rapidly in 1990 and 1991 (see Fig . 1 to Fig. 4). The grow th of
prices was moderate in the following years and it can be expected that the prices will not
increase so rapidly in the future. Ma rket oriented econom ies will have an increasingly
influence in Poland in the coming years.
The starting point has a significant influence on the future energy demand and supply
structure. As it is shown in Fig. 5 and Fig . 6, final energy consumption per capita in 1992
was almost half the average of OECD countries and electricity consumption was almost three
times low er. In Poland, energy intensity, based on PPP and referred to GDP , is more than 3
times higher (Figs. 7, 8), and electricity intensity is almost 3 times higher.
The major assumption of the approach is that the relationships between final energy
demand growth and macroeconomic growth characteristics in Poland, during the period 1995-
2020, will be similar to the ones that existed in developed countries during 1976-1992.
The approach may be formulated as follows:
1. Energ y/electricity demand forecas t is based on regres sion analysis relating energ y
demand and macroeconomic characteristics.
2. Macroeconomic and population growth is defined exogenously using output from
the SDM-NE Model.3. Specification and estimation of parameters to be used in the regression analysis
equations are based on developed (OEC D) co untries data; for selection of countries-
analogues time series 1970-1992 were used, for specification and parameter
estimation time series have been shortened to 1976 - 1992 to avoid taking first oil
crisis data.
4. The macroeconomic and energy sector characteristics of Poland are used for the
base year.
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Fig . 1 Indices of C oa l Prices , 1985 =1 00
GO OO GO GO GO GO GO GO GO OO CD CO CD CDCD CD CD CD CD CD CD CD CD CD CD CD CD CD
BELGIUM
FRANCE
GERMANY
POLAND
Fig . 2 Indices of Oi l Pro du cts P rices, 1985 =1 0 0
<=>OC D
11
O OC D
1—CMOOCD
—1cr>OOCD
—i —
ooCD
1LOCOCD
1t oC OC D
1
C OCD
1__COOOC D
1C DCOCD
1—C DC DC D
1i —
C DC D
1CMC DCD
1mC DCD
Fig . 3 Indices of Natural G as Pr ices, 1985 =1 00
1000800-.
600..
400..
200..
0 ^—r - >enCD
—i—TOOCD
1—
COOOCD
1—OT>GOCD
1—
GOCD
1
LOO OC D
1COC OC D
1
POC D
1
CO .O OCD
—=7=—
CDO OC D
1—
CDCDCD
—H—
C DC D
1—
CMCDC D
1COCDC D
Fig . 4 Indices of Electr ici ty Prices, 1985 =100
35 0300--
C O i C S J O O ^ L O C D I — O O C D C D i — C M O OC O O O O D O O O O G O O O O Q O O C O C D C D C D C DC D C D C O C D C D C D C D C D C D C D C D C D C D C D
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F ig . 5 Final Energy Consum ption per
<O 5OOOr
P o l a n d
O E C D c o u n t r i e s
CD i—CM n T to to r-~co co CDi—csjC O Q O O O O O C O C O C O C O O D C O C O C O C OCO CO CO CO CO CO CO CO CO CO CO CO CO
F ig . 6 Electrici ty Consumption per capita
10 OOG-
5 , 8 000S 6 ood-
4 000-
i2 OOOf
0
Poland
OECD countr ies
GO CO GO GO GO CO CO OO CO CO O"> CD CT?
en en en en cy> en en en en en CDen en
Fig. 7 Final Energy Intensity,
O 2 00
31 500
CD
§ 1 000-
0)oa-.500, .
Poland
OECD countries
^^H^ ^ ^J ^ ^ ^^j*1L J ^ ^ } ^ ^ ^3O C3J ^ 3 ' ••» ^ J
C O G O O D O D O D O O G O G O G O G O C n C O C Ocococococococococococococo
Fig . 8 Electricity Intensity,
500-
0
Poland
OECD countr ies
C D i—CM CO T"LT> CO r<~ OO O)CD i—C\JC O C O O O C O O O O O G O C O O D O O C O C O C OC O CO CO CO CO CO CO CO CO CO CO CO CO
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Finally, the method of energy demand forecasting leads to define variables andparam eters in the formula:
A£ igfe = (1+ A4 )" x (1+AB)P x Q-l
where i is the energy carrier subscript; k is the national economy sector subscript; A and B are
mac roecono mic variables; a, (5 and Q are param eters; A is a symbol of incremen t. Q is a
param eter describing all other (besides A and B) factors influencing the energy deman d g rowth.
It describes both technical and organisational standards of the given sector and conservation
programmes in energy use.
3. Data Sources
The basic source of statistical data used for forecasting were the publications of theInternational Energy Agency:
"National Accounts" volume II, OECD, Paris, Department of Economics and
Statistics;
"Energy Balances of OECD Countries", OECD, Paris;
"Energy Prices and Taxes", OECD, Paris.
In addition, several time series and other data were used, such as:
"Industrial Structure Statistics", OECD, Paris;"Year Urbanization Prospects", United Nations, Ne w York, 199 1;
"World Development R eport- Workers in an "integrating W orld", 1995 , Oxford
University Press.
These and other Polish sources have determined both energy carriers and economy
sectors aggregation:
Economy Sectors:
- Heavy Industry: Iron and Steel, Chem ical, Non-Ferrous Metals, Non-M etallic M inerals,
- Manufacturing - Other Industry Sectors, including Mining and Quarrying,
- Transport,
- Agriculture,
- Commerce and Public Sector,
- Households.
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Energy Carriers:
- Coal - Hard Coal, Lignite, Patent Fuel, Coke, Coke Oven Gas, Blast Furnace Gas,
- Oil - Crude Oil, Petroleum Products,
- Other Solid Fuels - Peat&Wood, Non-Commercial Fuels, Biomass, etc.,
- Gas - Natural Gas, Gas Works Gas,
- Electricity,
- Heat.
4. Countries-Analogues Set
First preliminary steps led to excluding several countries from the full list of OECD
countries. These coun tries were :
Iceland, Ireland and Luxembourg because their population was more than 10 times
lower than in Poland;
Australia, Canada, Ne'w Zealand and USA which are countries with quite different
geographical and economic conditions;
Turkey because of statistical data shortage and some inconsistency in the time
series:
For the remaining 16 countries, 1970-1992 time series of energy use/supply and
macroecono mics indices w ere investigated (Figs. 9-12). As expected, for the base year (1970 ),
energy production for many countries was similar to the Polish one, i.e., a significant share of
coal. There were however som e exceptions:
The contribution of gas is important in the Netherlands, and relatively important in
Austria and Italy;
Norway, where oil and gas are also significant, and Switzerland have a great
hydropower potential.
During the next step, changes in energy use/supply structure between 1970 and 1992
were considered. One sh ould no te that the energy supply structure in Poland has practically
remained unchanged from 1970 to 1992, and that, for the foreseeable future, the Polisheconomy will remain coal intensive. The share of coal production decreased more than 10
times in Belgium, Denmark and Japan, more than 20 times in The Netherlands; coal has been
replaced by nuclear energy in Belgium and Japan, and by oil and gas in The Netherlands and
Denm ark. In light of these chang es, these countries were also excluded from further
comparisons.
Therefore, for regression analysis, eight countries remained as analogues: Finland, Franc e,
Germany, Great Britain, Greece, Portugal, Spain and Sweden.
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F ig . 9 Primary Energy Production Structure,
1970
100% T
20%
0%
r~i
M Coal + Other S.
F.
H Nuclear D Hydro
a
OGas
M Ceolher.
Solar.etc.
F ig . 10 Primary Energ y Production Stru cture ,
1992
100%
I Cba/+ OtherS.
F.
i Nuclear
I Oi l
Hydro i Geothcr.
Solar.etc.
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Fig. 11 Primary Energy Supply Structure,1970
! Coal + Other S. MOil+P.P.F.
ElHydr iGeother. +
Solar.etc.
WCas
W L Electricity
i Nuclear
Fig. 12 Primary Energy Supply Structure,
1992
100%
20%
0%
Coal + | O t t e r
S. F.
Oil+P.P. DGas Nuclear
[jHydr Geo thcr. +
Sola r.etc.
Electricity
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5. Algorithm
Estimation of equation (1) is conducted in three stages:
1. Specification of variables and estimation of parameters for electricity;
2. Specification of variables and estimation of parameters for the rest of energy;
3. Breaking down of the rest of energy demand growth by energy carriers.
There are several reasons for distinguishing electricity from all other types of final
energy. For many purposes electricity cannot be practically replaced by any other ene rgy
carrier (lighting, electrical appliances, electromechanic devices, electrolyses, etc.). It is cho sen
as the most comfortable energy carrier, although it may be more expensive (w ashing m ach ines
with electrical water heating). As a consequ ence, electricity consumption and electricity
intensity steadily increases all over the wor ld whil e entire final energy intensity decreas es. A s
an illustration, indices of final energy and electricity intensity in Poland and in the European
countries of the OECD are shown in Fig. 13.
The following m acroeconomic characteristics w ere taken into consideration: GDP - Gross
Domestic Product, VA - Value Added in a particular sector, INV - Investment Level, INV_j -
Investment Level with one-year lag, and POP - Population.
Fig. 13 Final Energy / Electricity Intensity
Indices (1 97 0 = 100 )
2 0 -
0 -I f-oCO
~t -—H
O)
CD
CO
00
CD
O00CD
CM00CD
00CO
CO0 0
0 00 0o>
oCOCO
CMCDCO
years
- •— TF C - Poland
-m — EL - Poland
~O—TFC - OECD Europe
" O - f t . OECD Europe
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Electricity
A three-step estimation procedure was developed which combines historical data from
the eight selected countries and Polish forecast data from the most realistic scenario.
1. Equation (1) is estimated for each of the eight selected countries using time series
data from 1976 to 1992. The specification of the final regression equation takes
into consideration:
the goodness of fit between the empirical data and theoretical curves;
the similarity of predicted energy growth relative to the observed growth with
the analogue country;
sensible values of parameters a, P, and Q (in Table 1 parameter a connected
to value added growth in heavy industry for Portugal has negative sign).
Tab le 1 Est imat ion of Parameters for Electricity Demand Forecast in Indus t ry
a
PQ
Finland
V A
INV(-1)
0,6717
-0.0276
1.0121
France
V A
INV(-1)
0,4804
-0.0197
1.0017
Germany
mv(-i)
0.47*9
-0.2515
0.9932
Great
Britain
VA
INV(-1)
0,2021
0.0705
0.9907
Sweden
V A
INV(-1)
1.0141
0.0482
0.9804
Greece
VA
1.0143
0.9961
Spain •
V A
GDP
0.2575
-0.6272
1.0363
Portugal
VA
-0.0370
1.0413
2. Forecasts using Polish data are generated from the parameters for the eight
countries. In this step, tables of energy/electricity demand growth for each economy
sector are built and next, some countries are excluded from the list of analogues.
One takes into consideration mainly the consistency of predicted energy growth
relative to the expected growth in Poland (in Table 2, the growth of electricity
demand in the commercial sector calculated upon the time series of Greece amounts
to 14.554 which is rather unlikely).
After the second step only five analogues countries were retained: Finland, France,
Germ any, Great Britain, Sweden. Besides Finland, all these countries have a heavy industry.
3. Energy growth forecasts are averaged across countries and used as a dependent
variable for estimating equation (1). This regression is performed over the forecast
period using data from the macroeconomic model for Poland. Specified variables
A and B, and estimated parameters a, P, and Q are used for energy/electricity
demand growth forecast for all macroeconomic scenarios.
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Table 2. Estimation of Parameters for Electricity Demand Forecast
in Commerce & Public Sector
Time
Series
<x
Q
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
growth
rates
1992/
1970
Finland
POP
VA
7.3313
0.4789
1.0172
1.000
1.035
1.073
1.116
1.170
1.241
1.327
1.420
1.515
1.611
1.710
1.812
1.917
2.018
2.122
2.231
2.343
2.460
2.594
2.734
2.880
3.033
3.193
3.360
3.535
3.719
3.910
4.110
4.2
France
VA
INV(-1)
0.7919
-0.1055
L0289
1.000
1.032
1.066
1.101
1.151
1.225
1.320
1.421
1.524
1.627
1.733
1.841
1.954
2.071
2.193
2.320
2.453
2.591
2.735
2.885
3.042
3.205
3.377
3.555
3.742
3.937
4.140
4.352
4.51
Germany
POP
VA
-3.0462
1.8561
0.9694
1.000
0.980
0.968
0.956
0.966
1.017
1.106
1.200
1.294
1.378
1.456
1.530
1.602
1.674
1.744
1.813
1.881
1.947
2.007
2.065
2.121
2.177
2.231
2.284
2.335
2.385
2.432
2.479
2.64
Great
Britain
POP
VA
3.8147
0.2808
1.0005
1.000
1.010
1.021
1.034
1.052
1.077
1.108
1.141
1.172
1.202
1.232
1.261
1.289
1.315
1.341
1.366
1.392
1.417
1.446
1.476
1.506
1.535
1.566
1.596
1.627
1.658
1.690
1.721
1.95
Sweden
VA
INV
-0.0423
0.17&9
LO413
1.000
1.053
1.108
1.163
1.219
1.281
1.345
1.414
1.484
1.557
1.632
1.711
1.793
1.878
1.967
2.059
2.156_
2.256
2.361
2.470
2.584
2.703
2.827
2.956
3.091
3.232
3.379
3.533
3.1
Greece
POP
GDP
-0.2624
1.7553
L0324
1.000
1.115
1.222
1.320
1.427
1.598
1.816
2.084
2.362
2.655
2.970
3.309
3.675
4.073
4.504
4.970
5.476
6.023
6.612
7.249
7.938
8.683
9.487
10.356
11.292
12.301
13.387
14.554
4.98
Spain
POP
GDP
-4.0717
-0.00S2
.1.8771
1.000
1.069
1.143
1.220
1.302
1.387
1.479
1.575
1.679
1.790
1.908
2.034
2.169
2.319
2.479
2.650
2.833
3.028
3.228
3.442
3.670
3.9.13
4.172
4:448
4,742
5.056
5.391
5.748
6.3
Average
1.000
1.042
1.086
1.130
1.184
1261
1.357
1.465
1.576
1.689
1.806
1.928
2.057
2.192
2.336
2.487
2.648
2.817
2.998
3.189
3.391
3.607
3.836
4.079
4.338
4.613
4.904
5.214
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Other energy carriers
The estimation procedure for the other five energy carriers (coal, natural gas, petroleum
produc ts, other solid fue ls, heat) is similar to the one for electricity. The only difference tak es
place in the 3rd step: averaging energy growth across countries and estimation of parameters
are separated by a procedure of determination of the energy carriers shares in each economy
sector over the whole period of the studies.
Energy carriers shares
An "S"-country has been created- with the total sum of consumption of the five energy
carriers across Finlan d, G ermany, G reat Britain, and Sweden (France was excluded a s a co untry
that improperly s how s its heat use). In developing the procedure of breaking dow n en ergy
carriers the following facts were taken into account:
a significant share of heat in the base year in industry, manufacturing, commercial
and residential sector (see Table 3 comparing the shares in Poland and "S"-c ountry),
a relatively small share of oil products, especially in industry, manufacturing and
commercial sector.
Tab le 3. Shares of Energy Carriers
Heavy
Industry
Manufacturing
Transport
Agriculture
Commerce &
Public Sector
Households
Poland
"S"-
country
Poland
"S"-
country
Poland
"S"-
country
Poland
"S"-
country
Poland
"S"-
country
Poland
"S"-
country
1993
1976
1992
1993
1976
1992
1993
1976
1992
1993
1976
1992
1993
1976
1992
1993
1976
1992
Coal
0.490
0.480
0.365
0.268
0.250
0.262
0.036
0.038
0.001
0.218
0.099
0.042
0.718
0.439
0.203
0.472
0.351
0.183
Natural
Gas
0.130
0.081
0.252
0.060
0.052
0.263
0
0
0
0.051
0.002
0.035
0.096
0.147
0.258
0.140
0.085
0.381
Heat
0.346
0.000
0.006
0.597
0.006
0.031
0
0
0
0.097
0
0
0.163
0.012
0.086
0.275
0.011
0.063
Other
solid
fuels
0.001
0.005
0.005
0.019
0.075
0.123
0
0
0
0.105
0
0.051
0.022
0.025
0.023
0.107
0.031
0.031
Oil
products
0.034
0.434
0.373
0.055
0.618
0.320
0.963
0.962
0.999
0.529
0.899
0.872
0
0.377
0.427
0.006
0.523
0.341
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As it is improbable to reduce considerably the use of district heating and to expect a
significant increase of oil import, the rules of sharing energy carriers were the following:
changes o f coal, natural gas, and other solid fuels shares in the whole planning study
are equal to the ones of S-country during 1976 - 1992;
shares of oil products remain at their 1993 level;
shares of heat supplement to 100%.
5. Results
The results of the model consist of determining explanatory variables and parameters of
equa tion (1). The results for electricity are shown in Table 4.
Tab le 4. Results of estimation of equation (1) for electricity
Industry
Manufacturing
Transport
Agriculture
Commerce
& Public
Households
AE = (1+AV A)0 3 5 7 6
* (1+AINV.!)0 0 4 3 3
* 1.00064 - 1
A E = ( 1 + A V A ) 0 0 3 5 1 * ( 1 + A I N V . j ) 0 1 7 7 5 * 1.02908 - 1
AE = ( l + A I N V . / 0 3 2 8 * 0.99952 - 1
A E = ( 1 + A V A ) 0 5 2 5 6 * ( 1 + A I N V ) " 0 0 7 4 0 * 1.01956 - 1
A E = ( 1 + A V A ) 0 3 0 9 1 * ( 1 + A I N V . ! ) ' 0 0 1 2 0 * 1.03522 - 1
AE - (1+APO P)21
-5 1
* (1+AGDP)"0-
3189* 1.00637 - 1
As an illustration of the final energy demand growth forecast for realistic scenario,
predicted values for Heavy Industry and Commercial and Public Sector are shown in Fig. 14
and 15. Final energy elasticity (including electricity) is 0.236, electricity elasticity is 0.874.
Figures 16 and 17 compare energy and electricity intensities of 5 countries-analogues in
1970 and Poland in 1993. As expected, the energy intensity in 1993 for Poland w as
approxim ately 15 % higher than for Finland in 1970, and 55% higher than the average intensity
of 5 countries.
Figures 18 and 19 compare intensities of 5 countries in 1993 and evaluated inten sities
for Poland in 2020 (OP - Optimistic Scenario; RE - Realistic Scenario; PE - Pesimistic
Scen ario). One can note that the technology gap in energy use between countries-analogues
and Poland shrank, especially for the optimistic scenario.
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Fig. 14 Energy Demand Forecast
for Heavy Industry
6000 -r
5000 --
4 0 0 0 --
0)
2ooo
3000 -
2000 --
1000 -
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Fig. 15 Energy Demand Forecast
for Commercial Sector
6000 T
5000 -
4000 -
I I I I I I i l I I i
1000 -
o oC M CM
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Fig. 16 Energy Intensities 1970
O
ooo
0)
<DO
1.2
1 --
0.8 -
0.6 --
0.4 --
0.2 -
Finland Germany Great Britain Poland 1993
Fig. 17 Electricity Intensities 1970
0.14 j
0.12 --
O
© 0.08 +o
Finland Germany Great Britain Poland 1993
O Comparison by ER (3 Comparison by PPP
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F i g . 18 . Energy Intensity 1993
POLAND 2020 OP
POLAND 2020 RE
POLAND 2020 PE
UNITED KINGDOM
SWEDEN
GERMANY
FRANCE
FINLAND
50 100 150 200
[kg .oe/1000 US$]
250 300 350
F i g . 19 . Electricity Intensity 1993
POLAND 2020 OP
POLAND 2020 RE
POLAND 2020 PE
UNITED KINGDOM
SWEDEN
GERMANY
FRANCE
FINLAND
i l ll l ll l lP t w f t W ^
• • I
1
• '/* ' * - ' .•j
200 400 600
[kWh/1000 US$]
ER • PPP
800 1,000
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6. Conclusions
The method of energy demand growth forecasting combines mathematical expressions
in regression analysis equations and a planner intuition. The m ode l fulfills basic principles of
good long-term forecasting [1], namely:
Identify causality- energy demand growth is created by economic activity; the starting
point is strictly defined.
Be reproducible- the method is described in mathematical definitions and several
heuristic assumptions; it can be applied by another experienced planner, as well as to
another country in transition.
Be functional- it is used for determining energy demand g row th in Poland for 1994-2020
in conducted studies by using the BALANCE Module.
Test sensitivity- the model allows to reflect in energy dem and g rowth different scenarios
of macroeconomic growth, with faster or slower economy transformation.
Maintain simplicity- the model takes into account only basic appropriateness in energy
demand growth, it is simplified according to data availability and reliability.
Reference
[1] INTERN ATION AL ATOMIC ENERGY AGENCY, Expansion Planning for Electrical
Generating Systems - A Guidebook. IAEA, TRS No. 241, Vienna, 1984
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