Advances in Solar Forecasting: Computer Vision with Deep Learning: Article No. 100150

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

Research output: Contribution to journalArticlepeer-review

9 Scopus Citations


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.
Original languageAmerican English
Number of pages36
JournalAdvances in Applied Energy
StatePublished - 2023

NREL Publication Number

  • NREL/JA-5D00-86109


  • computer vision
  • deep learning
  • satellite imagery
  • sky images
  • solar forecasting
  • solar irradiance


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