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Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model

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  • Juan Du

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA)

  • Qilong Min

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA)

  • Penglin Zhang

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Jinhui Guo

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Jun Yang

    (State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China)

  • Bangsheng Yin

    (Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA)

Abstract

In this paper, we propose a novel forecast method which addresses the difficulty in short-term solar irradiance forecasting that arises due to rapidly evolving environmental factors over short time periods. This involves the forecasting of Global Horizontal Irradiance (GHI) that combines prediction sky images with a Radiative Transfer Model (RTM). The prediction images (up to 10 min ahead) are produced by a non-local optical flow method, which is used to calculate the cloud motion for each pixel, with consecutive sky images at 1 min intervals. The Direct Normal Irradiance (DNI) and the diffuse radiation intensity field under clear sky and overcast conditions obtained from the RTM are then mapped to the sky images. Through combining the cloud locations on the prediction image with the corresponding instance of image-based DNI and diffuse radiation intensity fields, the GHI can be quantitatively forecasted for time horizons of 1–10 min ahead. The solar forecasts are evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE) in relation to in-situ measurements and compared to the performance of the persistence model. The results of our experiment show that GHI forecasts using the proposed method perform better than the persistence model.

Suggested Citation

  • Juan Du & Qilong Min & Penglin Zhang & Jinhui Guo & Jun Yang & Bangsheng Yin, 2018. "Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model," Energies, MDPI, vol. 11(5), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1107-:d:144045
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    Cited by:

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    5. Shitao Wang & Mingjian Sun & Yi Shen, 2022. "Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed," Energies, MDPI, vol. 15(23), pages 1-15, November.
    6. 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).
    7. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    8. Manoel Henriques de Sá Campos & Chigueru Tiba, 2020. "Global Horizontal Irradiance Modeling for All Sky Conditions Using an Image-Pixel Approach," Energies, MDPI, vol. 13(24), pages 1-15, December.
    9. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
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