Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

A neural network for modelling photovoltaic modules using angle of incidence and clearness index is proposed. Engineers require methods to estimate the output of a photovoltaic plant depending on meteorological conditions. Therefore, models for the grid inverter and the generator must be provided, and their outputs must be combined. The connection between both models is related to the maximum power point of the generator and how it is tracked by the inverter. That maximum power point under specific conditions of irradiance and module temperature is determined by the I-V curve of the module, which must be simulated under those conditions. Algebraic procedures were used to simulate the I-V curve. Recently, neural networks have been used for the same purpose. Previous methods only take into account the irradiance and the module temperature. The model proposed is based on neural networks, and it uses not only the irradiance and the module temperature but also the angle of incidence and the instantaneous clearness index as additional inputs. The normalised clearness replaces the standard clearness index because it allows the removal of the hourly trend found in this last index. This new model improves the results obtained with previous ones as it can distinguish amongst samples with the same solar irradiance and temperature values but with different angle of incidence and instantaneous clearness index. Results show that this model could be used to improve the accuracy of the tools used to forecast the output of photovoltaic plants.