ARTIFICIAL INTELLIGENCE SOLUTION TO ELECTRICITY PRICE FORECASTING PROBLEM

Abstract

The market-clearing prices in deregulated electricity markets are volatile. Good market-clearing price forecasting will help producers and consumers to prepare their corresponding bidding strategies so as to maximize their profits. Market-clearing price prediction is a difficult task since bidding strategies used by market participants are complicated and various uncertainties interact in an intricate way. This article proposes the use of two artificial neural networks: the first to predict the day-ahead load and the second to forecast the day-ahead market-clearing prices. The methodology is applied to the California power market. After determining the optimal artificial neural network architecture with the minimum mean absolute percentage error on the test set, this architecture is used for price forecasting in periods with price spikes, for price forecasting for weekends, and for week-ahead MCP forecasting during the four seasons of the year. The forecasting accuracy of the artificial neural network model is compared with the accuracy of the persistence method and the results prove the efficiency and practicality of the proposed technique.