Effectiveness of neural networks and transfer learning to forecast photovoltaic power production

Artificial Neural Networks (ANNs) can successfully be integrated into smart models for energy prediction, but require large datasets for training. This investigation presents an innovative methodology for photovoltaic power generation forecasting with ANNs, when only a limited amount of real data is available, and has been tested and validated on a real-life photovoltaic installation. Feature selection identifies which meteorological features most impact photovoltaic power generation. A simulator, which accurately models a real photovoltaic installation, is used to create an artificial, but accurate and realistic, dataset of power generation large enough to effectively train and test different ANNs. These are then exploited on a portion of real, but limited, dataset of power generated by the real photovoltaic installation on which the simulator is modeled. Finally, different transfer learning techniques are used to tune the ANN models with the remaining portion of the real, but limited, dataset of photovoltaic power generation.