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Integration of intermittent renewable energy sources into electric grids inlarge proportions is challenging. A well-established approach aimed ataddressing this difficulty involves the anticipation of the upcoming energysupply variability to adapt the response of the grid. In solar energy,short-term changes in electricity production caused by occluding clouds can bepredicted at different time scales from all-sky cameras (up to 30-min ahead)and satellite observations (up to 6h ahead). In this study, we integrate thesetwo complementary points of view on the cloud cover in a single machinelearning framework to improve intra-hour (up to 60-min ahead) irradianceforecasting. Both deterministic and probabilistic predictions are evaluated indifferent weather conditions (clear-sky, cloudy, overcast) and with differentinput configurations (sky images, satellite observations and/or past irradiancevalues). Our results show that the hybrid model benefits predictions inclear-sky conditions and improves longer-term forecasting. This study lays thegroundwork for future novel approaches of combining sky images and satelliteobservations in a single learning framework to advance solar nowcasting.

Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions