Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts

Application of Machine Learning in forecasting renewable energy sources (RES) is increasing: In particular, several neural networks have been employed to perform the day-ahead photo-voltaic output power forecast. The aim of this paper is to consider different training approaches in order to improve the accuracy of the PV power prediction, with particular attention to day-ahead and intra-day forecasts. Additionally, novel error metrics, specifically proposed for the defined task, are compared with traditional ones, showing the best approach for the different considered cases. The results will be validated over a 1-year time range of experimentally measured data, for a PV module installed in the Solar Tech Lab in the department of Energy at Politecnico di Milano.