Forecasting Electicity in Photovoltaic Power Plants

Abstract Due to the increasing global demand for sustainable energy and the variable nature of solar radiation, accurate forecasting of photovoltaic (PV) system performance has become essential. This study focuses on the development of an artificial neural network (ANN) model to predict monthly electricity production in a rooftop photovoltaic power plant. The model uses meteorological inputs such as direct normal irradiation, diffuse radiation, and average monthly temperature, covering the period from January 2017 to December 2023. The ANN demonstrated high predictive accuracy and reliability, making it a valuable tool for energy management, planning, and integration of PV systems into power grids. While the dataset spans 2017–2023, the model is structured to allow generalization for future forecasting applications under similar input conditions.