Earth Notes: General Bibliography (paletta2023forecasting)
General public bibliography for EOU and related research. #bibliography #dataset
- [paletta2023forecasting] Quentin Paletta and Guillaume Arbod and Joan Lasenby Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions, , Applied Energy, volume 336, ISSN 0306-2619, doi:10.1016/j.apenergy.2023.120818, article/pages 120818 (article) (BibTeX).
keywords
Solar energy, Nowcasting, Computer vision, Deep learning, Satellite observations, Sky images
abstract
Integrating large proportions of intermittent renewable energy sources into electric grids 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 6~nbsp;h 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 innovative approaches of combining sky images and satellite observations in a single learning framework to advance solar nowcasting.
note
[Quote: "According to the IEA utility-scale solar power accounts for an increasing share of the yearly capacity additions. Contrary to residential photovoltaic electricity production whose spread out spatial distribution lessens the effects of local intermittency caused by clouds, output of large solar farms can be heavily impacted by local cloud cover changes."]