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A WGAN-GP Approach for Data Imputation in Photovoltaic Power Prediction

Author

Listed:
  • Zhu Liu

    (China Southern Power Grid Research Technology Co., Ltd., Guangzhou 510663, China)

  • Lingfeng Xuan

    (Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Qingyuan 513000, China)

  • Dehuang Gong

    (Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Qingyuan 513000, China)

  • Xinlin Xie

    (Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Qingyuan 513000, China)

  • Zhongwen Liang

    (Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Qingyuan 513000, China)

  • Dongguo Zhou

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

The increasing adoption of photovoltaic (PV) systems has introduced challenges for grid stability due to the intermittent nature of PV power generation. Accurate forecasting and data quality are critical for effective integration into power grids. However, PV power records often contain missing data due to system downtime, posing difficulties for pattern recognition and model accuracy. To address this, we propose a GAN-based data imputation method tailored for PV power generation. Unlike traditional GANs used in image generation, our method ensures smooth transitions with existing data by utilizing a data-guided GAN framework with quasi-convex properties. To stabilize training, we introduce a gradient penalty mechanism and a single-batch multi-iteration strategy. Our contributions include analyzing the necessity of data imputation, designing a novel conditional GAN-based network for PV data generation, and validating the generated data using frequency domain analysis, t-NSE, and prediction performance. This approach significantly enhances data continuity and reliability in PV forecasting tasks.

Suggested Citation

  • Zhu Liu & Lingfeng Xuan & Dehuang Gong & Xinlin Xie & Zhongwen Liang & Dongguo Zhou, 2025. "A WGAN-GP Approach for Data Imputation in Photovoltaic Power Prediction," Energies, MDPI, vol. 18(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1042-:d:1596404
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    References listed on IDEAS

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