Forecasting the power production from renewable energy sources (RESs) has become\nfundamental in microgrid applications to optimize scheduling and dispatching of the available assets.\nIn this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a\nPhysical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this\npaper is to provide a robust methodology to forecast 24 h in advance the PV power production in\na microgrid, addressing the specific criticalities of this environment. The proposed approach has\nto validate measured data properly, through an effective algorithm and further refine the power\nforecast when newer data are available. The procedure is fully implemented in a facility of the\nMulti-Good Microgrid Laboratory (MG2\nLab) of the Politecnico di Milano, Milan, Italy, where new\nEnergy Management Systems (EMSs) are studied. Reported results validate the proposed approach\nas a robust and accurate procedure for microgrid applications.
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