The energy produced by photovoltaic (PV) systems is deeply tied to the amount of received solar radiation. The intermittent nature of the solar power represents the primary problem for an efficient integration of PV systems into the electricity grid. The prediction of future solar radiation allows to quantify the energy production of the PV system, hence guaranteeing a more reliable and stable energy supply. Nevertheless, the availability of remote sensing imagery and machine learning techniques opens new possibilities to improve the solar radiation forecasting. In the literature, several studies have been done integrating satellite images to sophisticated deep learning models with the intent of improving the prediction of solar radiation forecasting, however the majority used satellite images to estimate cloud presence, cloud motion or to generate a solar radiation maps integrating ground measurements.