Forecasting power market clearing price and quantity using a neural network method

Abstract

Deregulation of the electric power industry worldwide raises many challenging issues. Forecasting the hourly market clearing prices (MCP) and quantities (MCQ) in daily power markets is the most essential task and basis for any decision making. One approach to predict the market behaviors is to use the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to "fit" and "extrapolate" the prices and quantities. Aiming at this challenging task, we developed a neural network method to forecast the MCPs and MCQs for the California day-ahead energy markets. The structure of the neural network is a three-layer back propagation (BP) network. The historical MCPs and MCQs of the California day-ahead energy market, the ISO load forecasts and other public information that may influence the markets are used for training, validating and forecasting test. Preliminary results show that our method is promising.