Day-Ahead System Marginal Price Forecasting Using Artificial Neural Network and Similar-Days Information

Abstract

Day-ahead system marginal price (SMP) forecasting constitutes essential information in the competitive energy market. Hence, this paper presents the development of a day-ahead SMP forecasting model via implementing an artificial neural network (ANN) algorithm. The accuracy of the ANN-based model is improved by including long-term historical data in addition to short-term historical data and by applying the k-fold cross-validation optimization algorithm. The selection of the short-term type input variable applies the Pearson correlation coefficient. Whereas the long-term type input variable is selected by applying the discrete Fréchet distance in conjunction with the information related to the season and type of the day to find the Similar-Days Index. In order to verify the model, the forecasted load and actual SMP for 15 years of historical data are used. The results indicate that the proposed model can forecast SMP with higher accuracy than the conventional forecasting model.