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

Zhu Liu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie and Dongguo Zhou ()
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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

Energies, 2025, vol. 18, issue 2, 1-14

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.

Keywords: PV output prediction; data imputation; GAN; LSTM; gradient penalty mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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