This paper proposes a data-driven approach to managing and optimizing a set of photovoltaic (PV) installations by exploiting the possibilities of spatio-temporal modeling and machine learning techniques. Given the variable nature of solar energy production, optimizing PV installations for maximum output and efficiency is crucial. The aim is to identify trends, patterns, challenges, and opportunities for improvement in the operation of multi-site PV systems as well as to provide information for optimal management of the lowvoltage network. A diverse array of methods are compared to forecast energy production, detect declines in system performance and refine maintenance scheduling. This study contributes to the growing field of renewable energy management by showcasing the effectiveness of ML models in optimizing a set of PV systems. It sets the stage for future progress in incorporating renewable energy sources into the electrical grid.