Enhancing the reliability of solar PV power modelling through HBA-optimized variational autoencoder and GRU neural networks
This paper introduces a variational autoencoder technique to construct an SPV power imputation model, aiming to fill in gaps in unobserved data, and the effectiveness of the proposed imputation and forecasting models is demonstrated with real-world SPV power data for imputation and forecasting tasks.