A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model.

ABSTRACT: Photovoltaic (PV) power is significantly influenced by meteorological fluctuations, and its forecasting accuracy is critical for power system dispatching and economic operation. To enhance forecasting precision, this paper proposes a hybrid framework integrating signal decomposition, parallel forecasting, and weight optimization. Firstly, the Thompson-Tau-Newton interpolation method is applied to handle missing data, and key meteorological factors are selected using the Pearson correlation coefficient to reduce input dimensionality. Secondly, the power sequence is decomposed into multi-scale subsequences using Ensemble Empirical Mode Decomposition (EEMD), which are then reconstructed into low-frequency components (reflecting trend features) and high-frequency components (capturing random f

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12361526 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature