IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p757-d1585357.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/757/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/3/757/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lou, Siwei & Li, Danny H.W. & Alshaibani, Khalid A. & Xing, Haowei & Li, Zhengrong & Huang, Yu & Xia, Dawei, 2022. "An all-sky luminance and radiance distribution model for built environment studies," Renewable Energy, Elsevier, vol. 190(C), pages 822-835.
    2. Alonso-Montesinos, J. & Martínez-Durbán, M. & del Sagrado, J. & del Águila, I.M. & Batlles, F.J., 2016. "The application of Bayesian network classifiers to cloud classification in satellite images," Renewable Energy, Elsevier, vol. 97(C), pages 155-161.
    3. Ruihua Si & Xintong Yan & Wanxun Liu & Ping Zhang & Mengdi Wang & Fengyong Li & Jiajia Yang & Xiangjing Su, 2025. "Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation," Energies, MDPI, vol. 18(3), pages 1-16, January.
    4. 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).
    5. YoungHyun Koo & Myeongchan Oh & Sung-Min Kim & Hyeong-Dong Park, 2020. "Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-19, January.
    6. Lukač, Niko & Mongus, Domen & Žalik, Borut & Štumberger, Gorazd & Bizjak, Marko, 2024. "Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance model," Applied Energy, Elsevier, vol. 353(PA).
    7. Guilherme Fonseca Bassous & Rodrigo Flora Calili & Carlos Hall Barbosa, 2021. "Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 14(19), pages 1-28, September.
    8. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:757-:d:1585357. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.