Renewable Energy Forecasting: a case study of a PV Solar Plant in a Small Island

The increase in power generation from uncertain renewable energy sources combined with the aleatory nature of loads is significantly affecting electrical network management, which aims to find a constant equilibrium between generated and consumed power. These problems are even more significant in geographical islands' networks disconnected from the main grid, where loads suffer a further variability due to the seasonal tourist influx. For these reasons an accurate forecasting of power generation is an essential tool to improve network's management. In this framework, the paper proposes a direct method for forecasting photovoltaic power generation. The method uses Long Short-Term Memory (LSTM) neural networks applied to real power and meteorological data of a PV power plant installed in an Italian small island, named Ustica.