Energy-Society Relation and Demand Prediction

8/21/2019 Energy-Society Relation and Demand Prediction

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Predicting the Electricity Demand

of Turkey Using the Past Data

Assist. Prof. M. Erdem Gnay

Energy Systems Engineering

Istanbul Bilgi University

[email protected]
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Energy and Society


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ENERGY AND SOCIETY

The more a country is developed themore energy it consumes.

Populationand wealthof a country

directly affect the energy consumption


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List of countries by population

Ref: http://en.wikipedia.org/wiki/List_of_countries_by_population


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Map of Countries:Gross Domestic Product

GDP is the addition of the consumer spending, investment spending, government

spending and the value of the exports and subtract the value of the imports .

This measure is often used to find out the health of a country in an economic way.

A country with a high value of GDP can be called a large economy.

Ref: http://simple.wikipedia.org/wiki/Gross_domestic_product


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Map of Countries:

CO2emissions

The more energy is consumed the more pollutants are

released.

Ref: http://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions


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ENERGY AND SOCIETY

The developed countries consume the most energyand produce the most pollution, primarily due tothe increase in the amount of energy per person.

Ref: Introduction to Renewable Energy, Abbas, Ghassemi, CRC Press


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Turkey is getting more populated

>20 Million

1950


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>30 Million

1960



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0

10

20

30

40

50

60

70

80

1935 1955 1975 1995 2015

Population(Millio

nPeople)

Year

>50 Million

1985



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0

10

20

30

40

50

60

70

80

1935 1955 1975 1995 2015

Population(Millio

nPeople)

Year

~80 Million

2014



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Gross Domestic Product Per Capita of

Turkey is Also in an Increasing Trend

0

5,000

10,000

15,000

20,000

1975 1985 1995 2005 2015

G

DP

percapita(I

nt$)

Year

GDP per

capita is anindicatorof theaverage

livingstandards
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Energy Demand of Turkey

The energy demand of Turkey increases year by year asthe population rises and as we become more and more

dependent on power hungry machines and devices.

50

100

150

200

250

300

1975 1985 1995 2005 2015

Electrici

tyDeman

d(TWh

)

Year~20 TWh

~250 TWh
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Energy Supply of Turkey in 2013

44%

25%

25%

4% 2%

Natural Gas

Coal

Hydroelectricity

Renewable

Energy

Liquid Fuels

Reference: Ministry of Energy and Natural Sources Web Page (www.enerji.gov.tr)


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Foreign Dependence of Energy of Turkey

Reference: Ministry of Energy and Natural Sources Strategic Plan 2010

% Dependence

on Foreign Countries

E D d f T k


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Problem:

The energy demand of Turkey is expected to increase

in an accelerating trend in the future.

Energy sources satisfying thisdemand will become totally

insufficient.

The foreign dependence ofenergy will even become more

serious in the future

E D d f T k


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Solution:

Investments on alternative energy sources must be encouraged.The local resources must be used more effectively

The types of energy sources must also be increased.

We should predict the future energydemandfrom the historical data

with a good accuracy.

We need to know the exact energydemand in the future years for these

plans to be effective


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Predicting the Energy Demand of Turkey

What happens if we cannot predict the futureelectricity demand accurately?

Overestimation would lead tounnecessary idle capacity thatmeans wasted financial resources.

Underestimation of thedemand would lead toblackouts, economic crisis etc.


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Predicting Energy Demand for the FutureWhich Factors May Affect the Demand?

We know that

Populationand

wealthof a

country directly

affect the

energy

consumption.


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Inflation?

It may have an

effect on theenergy demand.



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Summeraveragetemperature?

The hotter it isin the summerthe more we

use airconditioners.



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Constructing

theDatabase


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Year PopulationGDP per

capita ($)

Inflation

(%)

Avg T Sum

(oC)

Demand

(TWh)

1975 40347719 2020 16.26 23.13 15.7

1976 41225567 2306 19.33 21.64 18.6

1977 42103414 2481 46.77 23.03 21.1

2008 71517100 15021 10.06 24.64 198.1

2009 72561312 14550 6.53 23.78 194.1

2010 73722988 16003 6.40 25.22 210.4

2011 74724269 17781 10.45 23.87 230.3

2012 75627384 18315 6.16 24.69 242.4

2013 76667864 18834 7.40 23.85 245.5

2014 ? ? ? ? Unknown

2015 ? ? ? ? Unknown

? ? ? ? Unknown

? ? ? ? Unknown2020 ? ? ? ? Unknown

The Data

Used for

training

(constructingthe model)

Used for validation(checking whether

the model worksor not)

Turkishstatisticalinstitute

Used for Testing(predictingenergy demand

for the future)

OECD: Organisation forEconomic Co-operationand Development

Turkish StateMeteorological Service

Turkish ElectricityTransmission Company


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Demand (TWh)

Pop: Population

Gdp: gdp per capita (Int $)

Inf: Inflation (%)

Ts: Average summer temperature (oC)

Step 1: Constructing the Model:Linear Multiple Regression to Model theDemand between the Years 1975-2008

0 1 2 3 4infDemand a a pop a gdp a a Ts

Advantage of Multiple Regression: It is not a black box modeling i.e artificial neural

networks.

After determining the coefficients we can use the

equation for predicting the demand of any year.


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Modeling the Demand between for the Years 1975-2008

Variables Coefficient

Standard

Error of theCoefficient t value p value

constant -198 55.9 -3.54 0.0014

Population 1.60E-06 4.22E-07 3.80 0.00069

GDP 0.010 1.35E-03 7.30 0.00000

inflation -0.19 0.0552 -3.42 0.0019

Ts 5.58 2.51 2.22 0.034

The two-tailed P values are smaller

than 0.05 By conventional criteria, all the

variables are considered to bestatistically significant.


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Modeling the Demand between for the Years 1975-2008

6198 1.60 10

0.010 0.19 inf 5.58

Demand pop

gdp Ts

Demand (TWh)

Pop: Population

Gdp: gdp per capita (Int $)

Inf: Inflation (%)

Ts: Average summer temperature (oC)


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Step 2: Validating the Model:Linear Multiple Regression to Model theDemand between the Years 2009-2013

6198 1.60 10

0.010 0.19 inf 5.58

Demand pop

gdp Ts

Year PopulationGDP per

capita ($)

Inflation

(%)

Avg T Sum

(oC)

Demand

(TWh)

2009 72561312 14550 6.53 23.78 194.1

2010 73722988 16003 6.40 25.22 210.4

2011 74724269 17781 10.45 23.87 230.3

2012 75627384 18315 6.16 24.69 242.4

2013 76667864 18834 7.40 23.85 245.5


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We normally know the pop., gdp, inf. and Ts,between the years 2009-2013.

The problem is, we do not know the valuesof these in the future.

If we can correctly estimate them betweenthe years 2009-2013, then we can alsopredict them for the future years.

We need another tool to predict thecorresponding values.

Step 2: Validating the Model:Linear Multiple Regression to Model theDemand between the Years 2009-2013


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Autoregressive Regression

( ) .( 1), .( 2), .( 3)...Population t f Popl t Popl t Popl t

( ) ( 1), ( 2), ( 3)...GDP t f GDP t GDP t GDP t

For example the population in the year 2014 depends on

the population 2013, 2012, 2011 etc.

The same is true for GDP and other variables.

The autoregressive regression model specifies that an

output variable depends on its own previous values.


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Autoregressive Regression to Predict the

Population Between the Years 2009-2013

( ) 408779 1.01 ( 1)Population t Population t

0

20

40

60

80

1935 1955 1975 1995 2015

Popu

lation

(Millio

ns)

Year

Real Estimated Predicted

R2predicted=0.995

R2estimated=1.000


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Autoregressive Regression for to Predict the GDPper Capita Between the Years 2009-2013

( ) 7.97 1.06 ( 1)GDP t GDP t

0

5,000

10,000

15,000

20,000

1975 1985 1995 2005 2015

GDP

percap

ita

(In

t$)

Year


R2estimated=0.988

R2predicted=0.823


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Autoregressive Regression to Predict the

Inflation % Between the Years 2009-2013

2

( ) 1.00 1.40 ( 1) 0.0062 ( 1)Inf t Inf t Inf t

-20

0

20

40

60

80

100

120

140

1955 1975 1995 2015

In

flation%

Year


R2estimated=0.722

R2predicted=0.499


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Autoregressive Regression to Predict the AverageSummer Temperature Between the Years 2009-2013

( ) 1.72 0.45 ( 1) 0.22 ( 2) 0.09 ( 3)0.11 ( 4) 0.41 ( 5) 0.05 ( 6)

Ts t Ts t Ts t Ts t Ts t Ts t Ts t

21.5

22.0

22.523.0

23.5

24.0

24.5

25.0

25.5

1975 1985 1995 2005 2015AverageSumm

erTemperature(oC)

Year


R2estimated=0.597

R2predicted

=0.215


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Predicting the Electricity Demandfor the years Between 2009-2013

6198 1.60 10

0.010 0.19 inf 5.58

Demand pop

gdp Ts

Year Population GDP per capita ($)Inflation

(%)

Avg T Sum

(oC)

2009 72561384 15645 12.48 24.29

2010 73615026 16607 7.89 24.21

2011 74678109 17627 7.72 24.32

2012 75750718 18710 12.98 24.61

2013 76832939 19859 7.40 24.72


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50

100

150

200

250

300

1975 1985 1995 2005 2015

Electrici

tyDemand

(TWh)

Year


R2estimated=0.983

R2predicted=0.809


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190

210

230

250

270

290

2008 2010 2012 2014

Electrici

tyDemand

(TWh)

Year

Real Demand Predicted by the current model

R2=0.81

Average% Error = 3.1%

190

210

230

250

270

290

2008 2010 2012 2014

Electrici

tyDeman

d(TWh)

Year

Real Demand

EPDK High Demand Scenario

EPDK Low Demand Scenario

R2=0

Average

% Error = 13.5% R2=0Average

% Error = 10.2

Republic of Turkey Energy Market

Regulatory Authority Prediction

Overestimation

Current Model


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The data between 1975 to 2013 were usedto construct a multiple regression model.

Population, gdp per capita, inflation and

average summer temperature werepredicted for the years 2014-2020. These values were used to calculate the

energy demand.

Step 3: Using the Data of the Past to Predict

the Demand between the Years 2014-2020

6181 1.61 10

0.010 0.19 inf 4.80

Demand pop

gdp Ts


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80

160

240

320

1975 1984 1993 2002 2011 2020

Electr

icity

Demand

(TWh)

Y


R2estimated=0.991

~320 TWh

Step 3: Using the Data of the Past to Predict

the Demand between the Years 2014-2020

Energy-Society Relation and Demand Prediction

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