Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions
This work addresses the challenge of integrating intermittent solar energy into electric grids by improving intra-hour forecasting, though it appears incremental as it combines existing data sources in a new framework.
The study tackled the problem of predicting short-term solar energy variability by integrating sky images and satellite observations into a single machine learning framework for intra-hour irradiance forecasting, showing that the hybrid model improves predictions in clear-sky conditions and enhances longer-term forecasting.
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.