Statistical models approach for solar radiation prediction

It is well known that the knowledge of solar radiation represents a key for managing photovoltaic (PV) plants. In a smart grid scenario to predict the energy production can be considered a milestone. However, the unsteadiness of the weather phenomena makes the prediction of the energy produced by the solar radiation conversion process a difficult task. Starting from this considerations, the use of the data collected in the past represents only the first step in order to evaluate the variability both in a daily and seasonal fashion. In order to have a stronger dataset a multi-year observation is mandatory. In this paper, several autoregressive models are challenged on a two-year ground global horizontal radiation dataset measured in Milan, and the results are compared with those of simple predictor.