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A Long Short-Term Memory–Wasserstein Generative Adversarial Network-Based Data Imputation Method for Photovoltaic Power Output Prediction

Author

Listed:
  • Zhu Liu

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

  • Lingfeng Xuan

    (Qingyuan Yingde Power Supply Bureau, Guangdong Electric Power Co., Ltd., Yingde 513000, China)

  • Dehuang Gong

    (Qingyuan Yingde Power Supply Bureau, Guangdong Electric Power Co., Ltd., Yingde 513000, China)

  • Xinlin Xie

    (Qingyuan Yingde Power Supply Bureau, Guangdong Electric Power Co., Ltd., Yingde 513000, China)

  • Dongguo Zhou

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

Abstract

To address the challenges of the issue of inaccurate prediction results due to missing data in PV power records, a photovoltaic power data imputation method based on a Wasserstein Generative Adversarial Network (WGAN) and Long Short-Term Memory (LSTM) network is proposed. This method introduces a data-driven GAN framework with quasi-convex characteristics to ensure the smoothness of the imputed data with the existing data and employs a gradient penalty mechanism and a single-batch multi-iteration strategy for stable training. Finally, through frequency domain analysis, t-Distributed Stochastic Neighbor Embedding (t-SNE) metrics, and prediction performance validation of the generated data, the proposed method can improve the continuity and reliability of data in photovoltaic prediction tasks.

Suggested Citation

  • Zhu Liu & Lingfeng Xuan & Dehuang Gong & Xinlin Xie & Dongguo Zhou, 2025. "A Long Short-Term Memory–Wasserstein Generative Adversarial Network-Based Data Imputation Method for Photovoltaic Power Output Prediction," Energies, MDPI, vol. 18(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:399-:d:1569612
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    References listed on IDEAS

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    5. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
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    Cited by:

    1. Paolo Di Leo & Alessandro Ciocia & Gabriele Malgaroli & Filippo Spertino, 2025. "Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review," Energies, MDPI, vol. 18(8), pages 1-28, April.

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