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

• URL: http://arxiv.org/abs/2206.03207v1
• Date: Tue, 7 Jun 2022 11:52:09 GMT
• Title: Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions
• Authors: Quentin Paletta, Guillaume Arbod, Joan Lasenby
• Abstract summary: Integration of intermittent renewable energy sources into electric grids is challenging. Short-term changes in electricity production caused by occluding clouds can be predicted at different time scales. In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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