short-term load forecasting in electricity market
DESCRIPTION
Short-Term Load Forecasting In Electricity Market. Acknowledge: Dr. S. N. Singh ( EE ) Dr. S. K. Singh ( IIM-L ). N. M. Pindoriya Ph. D. Student (EE). TALK OUTLINE. Importance of STLF Approaches to STLF Wavelet Neural Network (WNN) Case Study and Forecasting Results. - PowerPoint PPT PresentationTRANSCRIPT
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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(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
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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
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