Short-Term Load Forecasting In Electricity Market N. M. Pindoriya Ph. D. Student (EE) Acknowledge: Dr. S. N. Singh ( EE ) Dr. S. K. Singh ( IIM-L )

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Presentation transcript:

Short-Term Load Forecasting In Electricity Market N. M. Pindoriya Ph. 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 pool 2) Bilateral Contract 3) Multilateral contract  Energy Markets: 1) Day-Ahead (Forward) Market 2) Hour-Ahead market 3) Real-Time (Spot) Market REACH Symposium

REACH Symposium (one hour to a week) Types of Load Forecasting Load Forecasting Short-TermMedium-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 REACH Symposium

Input data sources for STLF STLF Historical Load & weather data Real time data base Weather Forecast Information display Measured load EMS REACH Symposium

Approaches to STLF Hard computing techniques  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

Soft computing techniques  Artificial Neural Networks (ANNs),  Fuzzy logic (FL), ANFIS, SVM, etc…  Hybrid approach like Wavelet-based ANN Approaches to STLF REACH Symposium ANN Data Input Wavelet Decomposition Predicted Output ANN Wavelet Reconstruction ANN

Wavelet Neural Network REACH Symposium 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) REACH Symposium Input Layer Wavelet Layer Output Layer w1w1 w2w2 wmwm v1v1 v2v2   Product Layer   jj  ij x1x1 xnxn g  BP training algorithm has been used for training of the networks.

Mexican hat wavelet (a) Translated (b) Dilated REACH Symposium 2008

Case study SeasonsWinterSummer Historical hourly load data (Training) Jan. 2 – Feb. 18July 3 – Aug. 19 Test weeks Feb. 19 – Feb. 25Aug. 20 – Aug. 26 California Electricity Market, Year 2007  Data sets for Training and Testing REACH Symposium ( )

Case study REACH Symposium  Selection of input variables The hourly load series exhibits multiple seasonal patterns corresponding to daily and weekly seasonality.

Case study Hourly load Trend Daily and weekly Seasonality TemperatureExogenous variable Input variables to be used to forecast the load L h at hour h, REACH Symposium

REACH Symposium Case study

 Winter test week REACH Symposium

Case study  Summer test week REACH Symposium

WMAPEWeekly variance (10 -4 )R-Squared error CAISOANNAWNNCAISOANNAWNNCAISOANNAWNN Winter Summer Average REACH Symposium Case study  Statistical error measures

Thank you