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

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