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

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 da...

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Bibliographic Details

Published in:	Energies
Main Authors:	Zhu Liu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie, Dongguo Zhou
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Subjects:	PV output prediction data imputation GAN LSTM gradient penalty mechanism
Online Access:	https://www.mdpi.com/1996-1073/18/2/399