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Ultra-Short-Term Solar Irradiance Prediction Using an Integrated Framework with Novel Textural Convolution Kernel for Feature Extraction of Clouds

Author

Listed:
  • Lijie Wang

    (Department of Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

  • Xin Li

    (Department of Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

  • Ying Hao

    (Department of Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

  • Qingshan Zhang

    (Department of Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

Abstract

Solar irradiance is one of the main factors affecting photovoltaic power generation. The shielding effect of clouds on solar radiation is affected by both type and cover. Therefore, this paper proposes the use of textural features to represent the shielding effect of clouds on solar radiation, and a novel textural convolution kernel of a convolutional neural network, based on grey-level co-occurrence matrix, is presented to extract the textural features of clouds. An integrated ultra-short-term solar irradiance prediction framework is then proposed based on feature extraction network, a clear sky model, and LSTM. The textural features are extracted from satellite cloud images, and the theoretical irradiance under clear sky conditions is calculated based on an improved ASHRAE model. The LSTM is trained with the textural features of clouds, theoretical irradiance, and NWP information. A case study using data from Wuwei PV station in northwest China indicate that the features extracted from the proposed textural convolution kernel are better than common convolution kernels in reflecting the shielding effect of clouds on solar irradiance, and integrating textural features of cloud with theoretical irradiance can lead to better performance in solar irradiance prediction. Thus, this study will help to forecast the output power of PV stations.

Suggested Citation

  • Lijie Wang & Xin Li & Ying Hao & Qingshan Zhang, 2025. "Ultra-Short-Term Solar Irradiance Prediction Using an Integrated Framework with Novel Textural Convolution Kernel for Feature Extraction of Clouds," Sustainability, MDPI, vol. 17(6), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2606-:d:1613279
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    References listed on IDEAS

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    1. Cheng, Hsu-Yung, 2017. "Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting," Renewable Energy, Elsevier, vol. 104(C), pages 281-289.
    2. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
    3. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    4. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    5. Ismael, Sherif M. & Abdel Aleem, Shady H.E. & Abdelaziz, Almoataz Y. & Zobaa, Ahmed F., 2019. "State-of-the-art of hosting capacity in modern power systems with distributed generation," Renewable Energy, Elsevier, vol. 130(C), pages 1002-1020.
    6. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    7. 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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