energy demand forecasts

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.

107



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.

108



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

109



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

110



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.

I l l



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.

112



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.

113



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

114



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

115



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.

116



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

117



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

118



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.

119



Fig. 14 Energy Demand Forecast

for Heavy Industry

6000 -r

5000 --

4 0 0 0 --

0)

2ooo

3000 -

2000 --

1000 -

120



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

121



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

122



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

123



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

124

energy demand forecasts

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