Download - Stlf Reach 08
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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