Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules

This article presents the artificial neural network modelling of the operating current of a 120 Wp of mono-crystalline photovoltaic module. As an alternative method to analytical modelling approaches, this study uses the advantages of neural networks such as no required knowledge of internal system parameters, less computational effort and a compact solution for multivariable problems. Generalised regression neural network model is used in the present article to predict the operating current of the photovoltaic module. To show its merit, the current predicted from the artificial neural network modelling is compared to that from the analytical model. The five-parameter analytical model is drawn from the equivalent electrical circuit that includes light-generated current, diode reverse saturation current, and series and shunt resistances. The operating current predicted from both the neural and analytical models are compared to the measured current. Results have shown that the artificial neural network modelling provides a better prediction of the current than the five-parameter analytical model.

► Analytical models are hard to implement as the exact values of parameters involved are not known.
► Generalised regression neural network model led to more accurate current prediction than the analytical model.
► The present neural network could be used for design and/or optimization purposes of photovoltaic energy systems.