A Comparative Study of Machine Learning Models for PV Energy Prediction in an Energy Community

Energy communities have recently gained significant attention as local entities that empower neighborhoods to contribute actively to the clean energy transition by adopting solar energy. However, the variability of weather conditions makes PV energy production highly unpredictable, emphasizing the need for accurate prediction and forecasting to ensure efficient operation and balance supply and demand. This study investigates the use of machine learning models to predict PV energy generation from multiple household rooftop photovoltaic (PV) systems within an energy community, with solar irradiance serving as the sole input parameter. Furthermore, various deep learning architectures were also explored to forecast solar radiation and determine the optimal model configuration. The results show that the Random Forest model performed better than the other models tested, achieving the lowest error metrics for PV energy prediction. For solar radiation forecasting, the GRU model demonstrates good performance compared the other models.