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

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 fluctuations) based on sample entropy. Furthermore, a parallel XGBoost-LSTM forecasting structure is constructed, XGBoost models the low-frequency components to capture global patterns, while LSTM processes the high-frequency components to learn temporal dependencies. Finally, the Snake Optimization (SO) algorithm is introduced to dynamically optimize the combination weights, enabling adaptive fusion of forecasting results. Experimental results demonstrate that the proposed model significantly outperforms standalone benchmark methods. In comparison with Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and the equal-weight assignment approach for high- and low-frequency component forecasting, the proposed SO algorithm attains the lowest forecasting errors. The proposed method provides a novel approach to high-precision PV power forecasting by integrating multi-modal feature fusion and optimized weight allocation.