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
importance to secure operation of smart grids. In this paper, we present a study on the use of fuzzy neural networks and their
application to the prediction of solar photovoltaic outputs. The new learning strategy is suited to any fuzzy inference model. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying unknown process with a good prediction of the observed time series.