Electricity price forecasting of deregulated market using Elman Neural Network

Abstract

Price forecasting is one of the main issues faced in deregulated market because of the dynamic behaviour of the electricity prices. In a day-ahead pool market, market participants need forecasted prices to submit their bids to the market operator. Accurate forecast can provide a risk free environment for the producers and consumers to invest into the market. Participants themselves feel that they can have assured return if the forecasted prices are accurate. This paper presents Elman Neural Network to forecast the dynamics in the electricity prices accurately. The proposed method has been tested on Mainland Spain market to forecast the market clearing prices and found to be an efficient method in comparison with many existing methods.