short-term load forecasting in electricity market

Short-Term Load Forecasting In Electricity Market

N. M. PindoriyaPh. D. Student (EE)

Acknowledge:Dr. S. N. Singh (EE)Dr. S. K. Singh (IIM-L)

TALK OUTLINE

Importance of STLF

Approaches to STLF

Wavelet Neural Network (WNN)

Case Study and Forecasting Results

Introduction

Electricity Market (Power Industry Restructuring)

Objective: Competition & costumer’s choice

Trading Instruments:1) The pool2) Bilateral Contract3) Multilateral contract

Energy Markets:1) Day-Ahead (Forward) Market2) Hour-Ahead market3) Real-Time (Spot) Market

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REACH Symposium 2008 2

(one hour to a week)

Types of Load Forecasting

Load Forecasting

Short-Term Medium-Term(a month up to a year)

Long-Term(over one year)

In electricity markets, the load has to be predicted with the highest possible precision in different time horizons.

Importance of STLF

STLF

System Operator Economic load dispatch Hydro-thermal coordination System security assessment

Unit commitment Strategic bidding Cost effective-risk

management

Generators

LSE Load scheduling Optimal bidding

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Input data sources for STLF

STLF

Historical Load & weather data

Real time data base

Weather Forecast

Information display

Measured load

EMS

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Approaches to STLF

Hard computingHard computing techniquestechniques Multiple linear regression, Time series (AR, MA, ARIMA, etc.) State space and kalman filter.

× Limited abilities to capture non-linear and non-stationary characteristics of the hourly load series.

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Soft computing techniquesSoft computing techniques Artificial Neural Networks (ANNs), Fuzzy logic (FL), ANFIS, SVM, etc… Hybrid approach like Wavelet-based ANN

Approaches to STLF

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ANNANNData InputData Input

Wavelet Decomposition

Wavelet Decomposition

Predicted OutputPredicted Output

ANNANN

Wavelet Reconstruction

Wavelet Reconstruction

ANNANN

Wavelet Neural Network

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WNN combines the time-frequency localization characteristic of wavelet and learning ability of ANN into a single unit.

Adaptive WNN Fixed grid WNN•Activation function (CWT) • Activation function (DWT)

• Wavelet parameters and weights are optimized during training

• Wavelet parameters are predefined and only weights are optimized

WNN

Adaptive Wavelet Neural Network (AWNN)

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InputLayer

Wavelet Layer

OutputLayer

w1

w2

wm

v1

v2

Product Layer

j

ij

x1

xn

1 1

m n

j j i ij i

y w v x g

g

BP training algorithm has been used for training of the networks.

-8 -6 -4 -2 0 2 4 6 8-0.5

0

0.5

1

(x

)

x

t = 0t = 1t = 2

-8 -6 -4 -2 0 2 4 6 8-0.5

0

0.5

1

(x

)

x

a = 2a = 1a = 0.5

Mexican hat wavelet (a) Translated (b) Dilated

REACH Symposium 2008

Case study

Seasons Winter Summer

Historical hourlyload data (Training)

Jan. 2 – Feb. 18 July 3 – Aug. 19

Test weeks

Feb. 19 – Feb. 25 Aug. 20 – Aug. 26

California Electricity Market, Year 2007

Data sets for Training and Testing

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(http://oasis.caiso.com/ )

Case study

0 24 48 72 96 120 144 168 192-0.4

-0.2

0

0.2

0.4

0.6

0.8

Lag

Sam

ple

Aut

ocor

rela

tion

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Selection of input variables

The hourly load series exhibits multiple seasonal patterns corresponding to daily and weekly seasonality.

1 168 336 504 672 74420

25

30

35

Hours

Loa

d (G

W)

Case study

Hourly load

Trend

Daily and weekly Seasonality

Temperature Exogenous variable

1 2 3, ,h h hL L L

Input variables to be used to forecast the load Lh at hour h,

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23 24 48 72 96

120 144 168 169 192

, , , , ,

, , , ,h h h h h

h h h h h

L L L L L

L L L L L

1 2 3, ,h h hT T T

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Case study

10 20 30 40 50 60 70 80 90 1000

0.05

0.1

0.15

0.2

0.25

No.of iterations

mse

AWNNANN

Case study

Winter test week

0 24 48 72 96 120 144 168

30

22

24

26

28

30

32

Hours

Loa

d (G

W)

ActualANNCAISOAWNN

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Case study

Summer test week

0 24 48 72 96 120 144 16823

27

31

35

39

43

45

Hours

Load

(GW

)

ActualANNCAISOAWNN

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WMAPE Weekly variance (10-4) R-Squared error

CAISO ANN AWNN CAISO ANN AWNN CAISO ANN AWNN

Winter 1.774 1.849 0.825 2.429 3.220 0.713 0.9697 0.9540 0.9917

Summer 1.358 1.252 0.799 2.115 1.109 0.369 0.9889 0.9923 0.9975

Average 1.566 1.551 0.812 2.272 2.164 0.541 0.9793 0.9732 0.9946

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Case study

Statistical error measures

Thank you