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Short-Term Load Forecasting In Electricity Market

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.

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Short-Term Load Forecasting In Electricity Market

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  1. 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)

  2. TALK OUTLINE • Importance of STLF • Approaches to STLF • Wavelet Neural Network (WNN) • Case Study and Forecasting Results

  3. 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 20081

  4. Long-Term Short-Term Medium-Term Load Forecasting (a month up to a year) (over one year) Types of Load Forecasting In electricity markets, the load has to be predicted with the highest possible precision in different time horizons. (one hour to a week) REACH Symposium 20082

  5. Importance of STLF System Operator • Economic load dispatch • Hydro-thermal coordination • System security assessment Generators • Unit commitment • Strategicbidding • Cost effective-risk management STLF LSE • Load scheduling • Optimal bidding REACH Symposium 20083

  6. Input data sources for STLF Real time data base Weather Forecast Historical Load & weather data Measured load STLF Information display EMS REACH Symposium 20084

  7. 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 20085

  8. ANN Wavelet Decomposition Data Input Wavelet Reconstruction Predicted Output ANN ANN Approaches to STLF Soft computing techniques • Artificial Neural Networks (ANNs), • Fuzzy logic (FL), ANFIS, SVM, etc… • Hybrid approach like Wavelet-based ANN REACH Symposium 20086

  9. Wavelet Neural Network WNN combines the time-frequency localization characteristic of wavelet and learning ability of ANN into a single unit. WNN REACH Symposium 20087

  10. Adaptive Wavelet Neural Network (AWNN) Input Layer Wavelet Layer Product Layer Output Layer ij j  w1 x1 v1  w2  wm g v2 xn  • BP training algorithm has been used for training of the networks. REACH Symposium 20088

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

  12. Case study California Electricity Market, Year 2007 (http://oasis.caiso.com/ ) • Data sets for Training and Testing REACH Symposium 20089

  13. Case study • Selection of input variables • The hourly load series exhibits multiple seasonal patterns corresponding to daily and weekly seasonality. REACH Symposium 200810

  14. Case study • Input variables to be used to forecast the load Lh at hour h, REACH Symposium 200811

  15. Case study REACH Symposium 200812

  16. Case study • Winter test week REACH Symposium 200813

  17. Case study • Summer test week REACH Symposium 200814

  18. Case study • Statistical error measures REACH Symposium 200815

  19. Thank you

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