Several-hours-ahead electricity price and load forecasting using neural networks

In daily power markets, forecasting electricity prices and loads are the most essential task and basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several-hours-ahead electricity price and load forecasting using artificial intelligence method, such as neural network model, which uses publicly available data from NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of similar days. Two different ANN models, one for several-hours-ahead load forecasting and another for several-hours-ahead price forecasting have been proposed. The forecasted price and load from the neural network is obtained by adding a correction to the selected similar days, and the correction is obtained from the neural network. MAPE results show that several-hours-ahead electricity price and load in the deregulated Victorian market can be forecasted with reasonable accuracy.

[1]  T. Funabashi,et al.  Next day load curve forecasting using hybrid correction method , 2005, IEEE Transactions on Power Systems.

[2]  Zuyi Li,et al.  Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets , 2004 .

[3]  C. Rodriguez,et al.  Energy price forecasting in the Ontario competitive power system market , 2004, IEEE Transactions on Power Systems.

[4]  Shyh-Jier Huang,et al.  Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .

[5]  Peter B. Luh,et al.  An integrated neural network method for market clearing price prediction and confidence interval estimation , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[6]  T. Funabashi,et al.  One-Hour-Ahead Load Forecasting Using Neural Networks , 2002 .

[7]  C. N. Lu,et al.  A Data Mining Approach for Spatial Modeling in Small Area Load Forecast , 2002, IEEE Power Engineering Review.

[8]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[9]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[10]  K. Tomsovic,et al.  Discovering Price-Load Relationships in California's Electricity Market , 2001, IEEE Power Engineering Review.

[11]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[12]  D.W. Bunn,et al.  Forecasting loads and prices in competitive power markets , 2000, Proceedings of the IEEE.

[13]  T. Dillon,et al.  Electricity price short-term forecasting using artificial neural networks , 1999 .

[14]  Dipti Srinivasan,et al.  Parallel neural network-fuzzy expert system strategy for short-term load forecasting: system implementation and performance evaluation , 1999 .

[15]  Jinxiang Zhu,et al.  Forecasting energy prices in a competitive market , 1999 .

[16]  S. J. Kiartzis,et al.  A neural network short term load forecasting model for the Greek power system , 1996 .

[17]  S. Muto,et al.  Regression based peak load forecasting using a transformation technique , 1994 .

[18]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[19]  S..  A Generalized Knowledge-Based Short-Term Load-Forecasting Technique , .