A hybrid swarm-machine intelligence approach for day ahead price forecasting

Abstract

Accurate forecasting of the future electricity prices in deregulated markets has become the most important management goal since it forms the basis of maximising profits for the market participants. Electricity price forecasting, however is a complex task due to non-linearity, nonstationarity and volatility of the price signal. SVM is a machine intelligence technique that has good performance in terms of prediction. An optimum selection amongst a large number of various input combinations and parameters is a real challenge for any modeller in using SVMs. This study applies SVM to predict the hourly electricity prices of Ontario market. Optimal parameters of SVM are determined using swarm intelligence techniques. Some strategies are also developed specifically for day ahead market price forecasting considering data availability, the dynamics of price movement and forecasting horizon. A detailed analysis of a hybrid technique clubbing together the machine and swarm intelligence technique has been performed with different scenarios and strategies.