Multi-point solar prediction through feed-forward neural networks

In this paper we present a study on the feasibility of the prediction of the solar radiation on a location giving the meteorological measurement in surrounding locations on a mesoscale system scale. The data from four public stations run by the Lombardy regional agency for environmental protection (ARPA) have been used as dataset for training a neural network in order to predict with one-hour lag the global radiation in one station using the data from the other three stations. The results have been compared with other two models: the first makes use of only the data from the station to be predicted, while the second exploits all the available information considering all the four stations as input sources. The dataset has been formed using data from the ARPA stations in Milano, Crema, Osio sotto, e Cassano d'Adda, considering the years 2002-2007.