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Multi-model solar irradiance prediction based on automatic cloud classification

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  • Cheng, Hsu-Yung
  • Yu, Chih-Chang

Abstract

This paper proposes a framework to automatically conduct cloud classification on all-sky images and perform short-term solar irradiance prediction according to the classification results. The all-sky images are divided into blocks to deal with the mixed cloud type conditions. Local texture patterns and statistical texture features are extracted from the image blocks for cloud classification. Different cloud types with various heights, thickness, and opacity have different impact on the variation of solar irradiance. Therefore, several regression models are trained to capture the characteristics of irradiance changes under different cloud types. The current classified cloud type is used to select a corresponding prediction model. Such design substantially increases the prediction accuracy. The experimental results verify the effectiveness of the proposed framework. Both the proposed cloud classification method and irradiance prediction mechanism outperform existing works. Adding local texture patterns in the feature vector enhance the classification performance. Compared with non-block based methods, the proposed block-based method could increase the classification rate by 5%–10%. Utilizing multiple prediction models according cloud types could lower both the mean absolute error and the root mean squared error on short-term irradiance prediction.

Suggested Citation

  • Cheng, Hsu-Yung & Yu, Chih-Chang, 2015. "Multi-model solar irradiance prediction based on automatic cloud classification," Energy, Elsevier, vol. 91(C), pages 579-587.
    • Handle: RePEc:eee:energy:v:91:y:2015:i:c:p:579-587
    DOI: 10.1016/j.energy.2015.08.075
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    References listed on IDEAS

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    1. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Sian-Jing, 2014. "Bi-model short-term solar irradiance prediction using support vector regressors," Energy, Elsevier, vol. 70(C), pages 121-127.
    2. Martínez-Chico, M. & Batlles, F.J. & Bosch, J.L., 2011. "Cloud classification in a mediterranean location using radiation data and sky images," Energy, Elsevier, vol. 36(7), pages 4055-4062.
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    Cited by:

    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. 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.
    3. 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.
    4. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    5. García, Jesús M. & Padilla, Ricardo Vasquez & Sanjuan, Marco E., 2016. "A biomimetic approach for modeling cloud shading with dynamic behavior," Renewable Energy, Elsevier, vol. 96(PA), pages 157-166.

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