Neural network and Fuzzy Regression Model for Forecasting Short Term Price in Ontario Electricity Market

It is very important to forecast electricity price in a deregulated electricity market for choosing the bidding strategy, and it is the most important signal for other players. It engulfs information for both customers and producers in order to maximize their profit. Thus, choosing the best method of price forecasting is a crucial task to have the most accurate forecast. In this paper the price forecasting is done based on different methods including autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and fuzzy regression. The method is examined by using data of Ontario electricity market. The results of different methods are compared and the best method is chosen. Fuzzy regression model is a new method in forecasting and it is rare in the literature review; it is showed that it leads to the best results.