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Estimation of Solar Irradiance Under Cloudy Weather Based on Solar Radiation Model and Ground-Based Cloud Image

Author

Listed:
  • Yisen Niu

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Ying Su

    (Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China)

  • Ping Tang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Qian Wang

    (Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China)

  • Yong Sun

    (Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China)

  • Jifeng Song

    (Institute of Energy Power Innovation, North China Electric Power University, Beijing 102206, China)

Abstract

The estimation of solar radiation plays an important role in different fields such as heating, agriculture and energy. At present, most studies focus on clear-sky models; it is relatively difficult to quantify the obstruction of radiation by clouds, which makes the calculation of irradiance in cloudy weather more challenging. This paper proposes a method for calculating solar irradiance in cloudy weather, which consists of two parts: radiation and cloud. In the radiation part, clear-sky radiation and the distribution of all-sky irradiance under different haze conditions are studied. In the cloud part, a cloud transmittance model based on ground-based cloud images is studied. Then, combined with the radiation model, the calculation of Global Horizontal Irradiance (GHI) in cloudy weather is achieved. After testing, rRMSE of the clear-sky model for calculating Direct Normal Irradiance (DNI) and GHI is 4.48% and 5.62% respectively, the rRMSE of the all-sky model is 2.28%, and the rRMSE of the cloudy irradiance model is 16.74%.

Suggested Citation

  • Yisen Niu & Ying Su & Ping Tang & Qian Wang & Yong Sun & Jifeng Song, 2025. "Estimation of Solar Irradiance Under Cloudy Weather Based on Solar Radiation Model and Ground-Based Cloud Image," Energies, MDPI, vol. 18(3), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:757-:d:1585357
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    References listed on IDEAS

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    1. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    2. Niu, Yinsen & Song, Jifeng & Zou, Lianglin & Yan, Zixuan & Lin, Xilong, 2024. "Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold," Renewable Energy, Elsevier, vol. 226(C).
    3. García, Ignacio & de Blas, Marian & Hernández, Begoña & Sáenz, Carlos & Torres, José Luis, 2021. "Diffuse irradiance on tilted planes in urban environments: Evaluation of models modified with sky and circumsolar view factors," Renewable Energy, Elsevier, vol. 180(C), pages 1194-1209.
    4. João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    5. Yao, Wanxiang & Song, Mengjia & Li, Xianli & Meng, Xi & Wang, Yan & Kong, Xiangru & Jiang, Jinming, 2024. "A new modified method of all-sky radiance distribution based on the principle of photothermal integration," Applied Energy, Elsevier, vol. 367(C).
    6. Khalid Alshaibani & Danny Li & Emmanuel Aghimien, 2020. "Sky Luminance Distribution Models: A Comparison with Measurements from a Maritime Desert Region," Energies, MDPI, vol. 13(20), pages 1-12, October.
    7. Tzoumanikas, P. & Nikitidou, E. & Bais, A.F. & Kazantzidis, A., 2016. "The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system," Renewable Energy, Elsevier, vol. 95(C), pages 314-322.
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    Cited by:

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