Ricerc@Sapienza

High penetration level of intermittent and variable renewable electricity generation introduces signicant challenges
to energy management of modern smart grids. Solar photovoltaics and wind energy have uncertain and non-dispatchable
output which leads to concerns regarding the technical and economic feasibility of a reliable integration of large amounts of
variable generation into electric grids. In this scenario, accurate forecasting of renewable generation outputs is of paramount

Microgrids (MGs) are the most affordable solution for the development of smart grid infrastructures. They are conceived to intelligently integrate the generation from Distributed Energy Resources (DERs), to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well.

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.