Day-ahead price forecasting of electricity markets by a hybrid intelligent system

Price forecasting is so valuable for both producers and consumers in the new competitive electric power markets. In this paper, a new hybrid intelligent system (HIS) is proposed for day-ahead prediction of market clearing price (MCP) in the pool-based markets. MCP has a volatile and time-dependent behavior owning many outliers. Prediction of such a complex signal is a challenging task requiring a qualified forecast tool, which not only tits well to the training data, but also can predict the stochastic behavior of the unseen part of the signal. The proposed hybrid system is composed of a real-coded genetic algorithm (RCGA), cross-validation, repetitive training, and archiving techniques combined in the framework of a neural network (NN). The proposed RCGA with enhanced stochastic search capability is used for training of the NN while the cross-validation, repetitive training and archiving techniques enhances generalization capability of it. The proposed forecast strategy is examined on the different periods of the Spanish electricity market. It is shown that the method can provide more accurate results than the other price forecasting techniques such as ARIMA time series, wavelet-ARIMA, fuzzy neural network (FNN), and generalized auto-regressive conditional heteroskedastic (GARCH) methods. Copyright (C) 2008 John Wiley & Sons, Ltd.