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Bi-model short-term solar irradiance prediction using support vector regressors

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  • Cheng, Hsu-Yung
  • Yu, Chih-Chang
  • Lin, Sian-Jing

Abstract

This paper proposes an accurate short-term solar irradiance prediction scheme via support vector regression. Utilizing clearness index conversion and appropriate features, the support vector regression models are able to output satisfying prediction results. The prediction results are further improved by the proposed ramp-down event forecasting and solar irradiance refinement procedures. With the help of all-sky image analysis, two separated regression models are constructed based on the cloud obstruction conditions near the solar disk. With bi-model prediction, the behavior of the changing irradiance can be captured more accurately. Moreover, if a ramp-down event is forecasted, the predicted irradiance is corrected based on the cloud cover ratio in the area near the sun. The experiments have shown that the proposed method can effectively improve the prediction accuracy on a highly challenging dataset.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:121-127
    DOI: 10.1016/j.energy.2014.03.096
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    References listed on IDEAS

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    1. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Chih-Lung, 2021. "Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks," Renewable Energy, Elsevier, vol. 179(C), pages 2300-2308.
    2. 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.
    3. Cheng, Hsu-Yung, 2016. "Hybrid solar irradiance now-casting by fusing Kalman filter and regressor," Renewable Energy, Elsevier, vol. 91(C), pages 434-441.
    4. 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.
    5. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    6. Wen, Yan & Pan, Su & Li, Xinxin & Li, Zibo & Wen, Wuzhenghong, 2024. "Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach," Energy, Elsevier, vol. 299(C).
    7. 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.
    8. 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.
    9. 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.
    10. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.

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