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Data Augmentation-Based Photovoltaic Power Prediction

Author

Listed:
  • Xifeng Wang

    (Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China
    These authors contributed equally to this work.)

  • Yijun Shen

    (Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China
    These authors contributed equally to this work.)

  • Haiyu Song

    (School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Shichao Liu

    (Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
    These authors contributed equally to this work.)

Abstract

In recent years, as the grid-connected installed capacity of photovoltaic (PV) power generation has increased by leaps and bounds, it has assumed considerable importance in predicting PV power output. However, the power prediction of newly built small-scale PV power plants often suffers from many unexpected problems, such as data redundancy, data noise, data sample imbalance, or even missing key data. Motivated by the above facts, this paper proposes a data augmentation-based prediction framework for PV power. Firstly, the daily power distance measurement is used to analyze feature correlation and filter erroneous data. Then, the autoencoder network trained based on the random discarding mechanism is used to restore the PV power generation data. At the same time, a specific data augmentation method for the PV power curve is designed to eliminate the influence of data sample imbalance. In the final experimental section, compared with the latest method, this method achieved the highest MAE accuracy of 8.26% and RMSE accuracy of 10.96%, which proves the effectiveness of this method.

Suggested Citation

  • Xifeng Wang & Yijun Shen & Haiyu Song & Shichao Liu, 2025. "Data Augmentation-Based Photovoltaic Power Prediction," Energies, MDPI, vol. 18(3), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:747-:d:1584966
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    References listed on IDEAS

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    1. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2013. "Hybrid methodology for hourly global radiation forecasting in Mediterranean area," Renewable Energy, Elsevier, vol. 53(C), pages 1-11.
    2. Rose, Amy & Stoner, Robert & Pérez-Arriaga, Ignacio, 2016. "Prospects for grid-connected solar PV in Kenya: A systems approach," Applied Energy, Elsevier, vol. 161(C), pages 583-590.
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    1. Xiaohong Huang & Xiuzhen Ding & Yating Han & Qi Sima & Xiaokang Li & Yukun Bao, 2025. "Day-Ahead Photovoltaic Power Forecasting Based on SN-Transformer-BiMixer," Energies, MDPI, vol. 18(16), pages 1-27, August.

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