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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 on theme: "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 )"— Presentation transcript:

1 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 )

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 2008 1

4 REACH Symposium 2008 2 (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.

5 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 2008 3

6 Input data sources for STLF STLF Historical Load & weather data Real time data base Weather Forecast Information display Measured load EMS REACH Symposium 2008 4

7 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 2008 5

8 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 ANN Data Input Wavelet Decomposition Predicted Output ANN Wavelet Reconstruction ANN

9 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 and weights are optimized during training Wavelet parameters are predefined and only weights are optimized WNN

10 Adaptive Wavelet Neural Network (AWNN) REACH Symposium 2008 8 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.

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

12 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 2008 9 (http://oasis.caiso.com/ )http://oasis.caiso.com/

13 Case study REACH Symposium 2008 10  Selection of input variables The hourly load series exhibits multiple seasonal patterns corresponding to daily and weekly seasonality.

14 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 2008 11

15 REACH Symposium 2008 12 Case study

16  Winter test week REACH Symposium 2008 13

17 Case study  Summer test week REACH Symposium 2008 14

18 WMAPEWeekly variance (10 -4 )R-Squared error CAISOANNAWNNCAISOANNAWNNCAISOANNAWNN Winter1.7741.8490.8252.4293.2200.7130.96970.95400.9917 Summer1.3581.2520.7992.1151.1090.3690.98890.99230.9975 Average1.5661.5510.8122.2722.1640.5410.97930.97320.9946 REACH Symposium 2008 15 Case study  Statistical error measures

19 Thank you

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