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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)

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TALK OUTLINE

Importance of STLF

Approaches to STLF

Wavelet Neural Network (WNN)

Case Study and Forecasting Results

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Introduction

Electricity Market (Power Industry Restructuring)

Objective:Competition & costumers choice

Trading Instruments:

1) The pool2) Bilateral Contract

3) Multilateral contract Energy Markets:

1) Day-Ahead (Forward) Market

2) Hour-Ahead market

3) Real-Time (Spot) Market

REACH Symposium 2008 1

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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 thehighest possible precision in different time horizons.

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Importance of STLF

STLF

System Operator Economic load dispatch

Hydro-thermal coordination

System security assessment

Unit commitment

Strategicbidding

Cost effective-risk

management

Generators

LSE

Load scheduling

Optimal bidding

REACH Symposium 2008 3

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Input data sources for STLF

STLF

Historical Load &

weather data

Real time

data baseWeather

Forecast

Informationdisplay

Measured load

EMS

REACH Symposium 2008 4

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Approaches to STLF

Hard computingtechniques

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.

REACH Symposium 2008 5

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Soft computing techniques

Artificial Neural Networks (ANNs),

Fuzzy logic (FL), ANFIS, SVM, etc

Hybrid approach like Wavelet-based ANN

Approaches to STLF

REACH Symposium 2008 6

ANNData

Input

Wavelet

DecompositionPredicted

Output

ANN

Wavelet

Reconstruction

ANN

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Wavelet Neural Network

REACH Symposium 2008 7

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 andweights are optimized during

training

Wavelet parameters arepredefined and only weights

are optimized

WNN

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Adaptive Wavelet Neural Network (AWNN)

REACH Symposium 2008 8

Input

Layer

Wavelet

Layer

Output

Layer

w1

w2

wm

v1

v2

Product

Layer

j

ij

x1

xn

1 1

m n

j j i i

j i

y w v x g

g

BP training algorithm has been

used for training of the networks.

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

-0.5

0

0.5

1

(x)

x

t = 0

t = 1

t = 2

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

-0.5

0

0.5

1

(x)

x

a = 2

a = 1

a = 0.5

Mexican hat wavelet (a) Translated (b) Dilated

REACH Symposium 2008

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Case study

Seasons Winter Summer

Historical hourly

load data (Training)Jan. 2 Feb. 18 July 3 Aug. 19

Testweeks

Feb. 19 Feb. 25 Aug. 20 Aug. 26

California Electricity Market, Year 2007

Data sets for Training and Testing

REACH Symposium 2008 9

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

http://oasis.caiso.com/http://oasis.caiso.com/http://oasis.caiso.com/http://oasis.caiso.com/http://oasis.caiso.com/http://oasis.caiso.com/http://oasis.caiso.com/http://oasis.caiso.com/

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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

Sample

Autocorrelation

REACH Symposium 2008 10

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

L

oad(GW)

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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 loadLh at hour h,

REACH Symposium 2008 11

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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REACH Symposium 2008 12

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

AWNN

ANN

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Case study

Winter test week

0 24 48 72 96 120 144 168

30

22

24

26

28

30

32

Hours

Load(GW)

Actual

ANN

CAISO

AWNN

REACH Symposium 2008 13

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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

REACH Symposium 2008 15

Case study

Statistical error measures

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Thank you