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Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather

Author

Listed:
  • Zhiying Lu

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300372, China)

  • Wenpeng Chen

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300372, China)

  • Qin Yan

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300372, China)

  • Xin Li

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300372, China)

  • Bing Nie

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300372, China)

Abstract

Haze constitutes a pivotal meteorological variable with notable implications for photovoltaic power forecasting. The presence of haze is anticipated to lead to a reduction in the output power of photovoltaic plants. Therefore, achieving precise forecasts of photovoltaic power in hazy conditions holds paramount significance. This study introduces a novel approach to forecasting photovoltaic power under haze conditions, leveraging ground-based cloud images. Firstly, the aerosol scattering coefficient is introduced as a pivotal parameter for characterizing photovoltaic power fluctuations influenced by haze. Additionally, other features, such as sky cloud cover, color attributes, light intensity, and texture characteristics, are considered. Subsequently, the Spearman correlation coefficient is applied to calculate the correlation between feature sequences and photovoltaic power. Effective features are then selected as inputs and three models—LSTM, SVM, and XGBoost—are employed for training and performance analysis. After comparing with existing technologies, the predicted results have achieved the best performance. Finally, using actual data, the effectiveness of the aerosol scattering coefficient is confirmed, by exhibiting the highest correlation index, as a pivotal parameter for forecasting photovoltaic output under the influence of haze. The results demonstrate that the aerosol scattering coefficient enhances the forecast accuracy of photovoltaic power in both heavy and light haze conditions by 1.083% and 0.599%, respectively, while exerting minimal influence on clear days. Upon comprehensive evaluation, it is evident that the proposed forecasting method in this study offers substantial advantages for accurately predicting photovoltaic power output in hazy weather scenarios.

Suggested Citation

  • Zhiying Lu & Wenpeng Chen & Qin Yan & Xin Li & Bing Nie, 2023. "Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16233-:d:1286232
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    References listed on IDEAS

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    1. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    2. Kamadinata, Jane Oktavia & Ken, Tan Lit & Suwa, Tohru, 2019. "Sky image-based solar irradiance prediction methodologies using artificial neural networks," Renewable Energy, Elsevier, vol. 134(C), pages 837-845.
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