Application of artificial neuronal networks for the prediction of monthly photovoltaic power using tilt angle as input in Rabat city, Morocco

Abstract

The use of artificial neural networks (ANNs) is attracting interest in the field of renewable energies, particularly for predicting operational parameters of photovoltaic (PV) systems. Thus, the present study aims to develop an ANN model for predicting the output power of a photovoltaic module. In this context, two ANN models with two and three hidden layers, respectively, were trained and validated to predict the average monthly photovoltaic power Pp produced by a 0. 325 kW photovoltaic module by varying the tilt angle from 0° to 90° of Rabat city, Morocco. The training data used in this study include longitude, latitude, calculated photovoltaic power, tilt angle, and months as inputs, while the predicted Pp is the target. These two proposed models are trained using the Levenberg-Marquardt (LM) optimiser. Furthermore, to confirm the reliability of each of these two proposed ANN models, existing photovoltaic power data were used to validate the two proposed models by predicting the average monthly power of the photovoltaic module. For the tilt angle of 34°, which corresponds to the latitude of the city of Rabat, the ANN model with three hidden layers performed better with an R2 value of 0.9997.