A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images

CVPRW 2020 pp. 2685-2688

doi:10.1109/CVPRW50498.2020.00323 /cvprw/2020/guen2020cvprw-deep/

Abstract

We present a new deep learning approach for short-term solar irradiance forecasting based on fisheye images. Our architecture, based on recent works on video prediction with partial differential equations, extracts spatio-temporal features modelling cloud motion to accurately anticipate future solar irradiance. Our method obtains state-of-the-art results on video prediction and 5min-ahead irradiance forecasting against strong recent baselines, highlighting the benefits of incorporating physical knowledge in deep models for real-world physical process forecasting.

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Cite

Text

Le Guen and Thome. "A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00323

Markdown

[Le Guen and Thome. "A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/guen2020cvprw-deep/) doi:10.1109/CVPRW50498.2020.00323

BibTeX

@inproceedings{guen2020cvprw-deep,
  title     = {{A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images}},
  author    = {Le Guen, Vincent and Thome, Nicolas},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {2685-2688},
  doi       = {10.1109/CVPRW50498.2020.00323},
  url       = {https://mlanthology.org/cvprw/2020/guen2020cvprw-deep/}
}