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Short-term solar radiation forecasting with a novel image processing-based deep learning approach

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  • Eşlik, Ardan Hüseyin
  • Akarslan, Emre
  • Hocaoğlu, Fatih Onur

Abstract

— In this study, an image processing-based deep learning approach for short-term forecast of solar radiation has been developed. For this purpose, firstly, cloud movements occurred during the day are tracked and future cloud movements are forecasted, accordingly. Subsequently, using the cloud motion estimation and extraterrestrial solar radiation data, 1-min averaged solar radiation values are estimated for 5-min time horizon. Shi-Tomasi method is employed to determine the feature points to be tracked on the sky images whereas, Lucas-Kanade optical flow method is employed to track the determined feature points on the sequential images. Average cloud velocity and directions are calculated by the help of linear regression method from tracked cloud movements. A hybrid approach including K-means and red/blue ratio is built to classify the pixels of the image whether they are clouds or sky. Finally, short-term solar radiations are estimated using the Long-Short Term Memory (LSTM) deep learning method. The performance of the proposed approach is compared with other methods in the literature. As a result it is concluded that, developed approach outperforms most methods in the literature with RMSE values of 47.576, 53.830, 68.103, and 92.386 for four different days and can be used as an alternative approach.

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  • Eşlik, Ardan Hüseyin & Akarslan, Emre & Hocaoğlu, Fatih Onur, 2022. "Short-term solar radiation forecasting with a novel image processing-based deep learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 1490-1505.
    • Handle: RePEc:eee:renene:v:200:y:2022:i:c:p:1490-1505
    DOI: 10.1016/j.renene.2022.10.063
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    3. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
    4. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    5. Nunes Maciel, Joylan & Javier Gimenez Ledesma, Jorge & Hideo Ando Junior, Oswaldo, 2024. "Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Zhang, Liwenbo & Wilson, Robin & Sumner, Mark & Wu, Yupeng, 2023. "Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach," Renewable Energy, Elsevier, vol. 216(C).
    7. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).

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