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 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.