Advances in solar forecasting: Computer vision with deep learning

Paletta, Quentin; Terrén-Serrano, Guillermo; Nie, Yuhao; Li, Binghui; Bieker, Jacob; Zhang, Wenqi; Dubus, Laurent; Dev, Soumyabrata; Feng, Cong

doi:10.1016/j.adapen.2023.100150

Title: Advances in solar forecasting: Computer vision with deep learning

Journal Article · 06 August 2023 · Advances in Applied Energy

DOI: https://doi.org/10.1016/j.adapen.2023.100150 · OSTI ID:1997245

^[1]; Terrén-Serrano, Guillermo ^[2]; Nie, Yuhao ^[3]; Li, Binghui ^[4]; Bieker, Jacob ^[5]; Zhang, Wenqi ^[6]; Dubus, Laurent ^[7]; Dev, Soumyabrata ^[8]; Feng, Cong ^[6]

University of Cambridge (United Kingdom); European Space Research Institute (Italy); European Space Agency (United Kingdom); ENGIE Lab CRIGEN (France)
University of California, Santa Barbara, CA (United States)
Stanford University, CA (United States)
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Open Climate Fix (United Kingdom)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Réseau de Transport d’Électricité (France); World Energy & Meteorology Council (United Kingdom)
University College Dublin (Ireland)

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: European Space Agency; European Space Research Institute; Science Foundation of Ireland; USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC36-08GO28308; AR0001289

OSTI ID:: 1997245

Report Number(s):: NREL/JA-5D00-86109; MainId:86882; UUID:a8a0d477-53f0-44c3-8a9a-100b5407eee0; MainAdminID:70317

Journal Information:: Advances in Applied Energy, Journal Name: Advances in Applied Energy Vol. 11; ISSN 2666-7924

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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