Neural network approaches to electricity price forecasting in day-ahead markets

Forecasting electricity prices is today an essential tool in the day-ahead competitive market. Prediction techniques based on neural and fuzzy neural networks are very promising in terms of prediction performance and model accuracy. In this paper, we investigate the applicability to the electricity market of three well-known approaches, namely Radial Basis Function neural networks, Mixture of Gaussian neural networks and Higher-Order Neuro-Fuzzy Inference System. Through a set of real-world examples we assess the applicability of such methodologies for medium-term energy price projections.