Short-term irradiance forecasting on the basis of spatially distributed measurements

The output power of photovoltaic (PV) systems is heavily influenced by mismatching conditions that can drastically reduce the power produced by PV arrays. The mismatching power losses in PV systems are mainly related to partial or full shading conditions, i.e. non-uniform irradiation of the array. An essential point is the detection of the irradiance level in the whole PV plant. The use of irradiance sensors is generally avoided because of their cost and necessity for periodic calibration. In this work, an Artificial Neural Network (ANN) based method is proposed to forecast the irradiance value of each panel constituting the PV module, starting from a number of spatially distributed analytical irradiance computations on the array. A 2D random and cloudy 12 h irradiance profile is generated considering wind action; the results show that the implemented system is able to provide an accurate temporal prevision of the PV plant irradiance distribution during the day.