  Integration of intermittent renewable energy sources into electric grids in
large proportions is challenging. A well-established approach aimed at
addressing this difficulty involves the anticipation of the upcoming energy
supply variability to adapt the response of the grid. In solar energy,
short-term changes in electricity production caused by occluding clouds can be
predicted 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 these
two complementary points of view on the cloud cover in a single machine
learning framework to improve intra-hour (up to 60-min ahead) irradiance
forecasting. Both deterministic and probabilistic predictions are evaluated in
different weather conditions (clear-sky, cloudy, overcast) and with different
input configurations (sky images, satellite observations and/or past irradiance
values). Our results show that the hybrid model benefits predictions in
clear-sky conditions and improves longer-term forecasting. This study lays the
groundwork for future novel approaches of combining sky images and satellite
observations in a single learning framework to advance solar nowcasting.
