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A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images

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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https://hal.archives-ouvertes.fr/hal-02947332
Contributor : Vincent Le Guen <>
Submitted on : Wednesday, September 23, 2020 - 10:17:19 PM
Last modification on : Friday, October 2, 2020 - 10:32:02 PM
Long-term archiving on: : Thursday, December 3, 2020 - 4:25:09 PM

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Vincent Le Guen, Nicolas Thome. A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images. CVPR OmniCV worshop 2020, Jun 2020, Seattle, United States. ⟨10.1109/CVPRW50498.2020.00323⟩. ⟨hal-02947332⟩

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