Skip to Main Content
In a power market, the price of electricity is the most important signal to all market participants. However, electricity price forecast is a complex task due to non-linearity, non-stationarity and volatility of the price signal. In spite of all performed research on this area in the recent years, there is still an essential need for more accurate price forecast methods. Besides, there is a lack of robust feature selection technique for designing the input vector of electricity price forecast, which can consider the non-linearities of the price signal. In this study, a new price forecast method is proposed, which is composed of a modified version of Relief algorithm for feature selection and a hybrid neural network for prediction. The proposed approach is examined on Ontario, New England and Italian electricity markets and compared with some of the most recently published price forecast methods.