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, parall...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16368-9 |
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| author | Ying Xu Xinrong Ji Zhengyang Zhu |
| author_facet | Ying Xu Xinrong Ji Zhengyang Zhu |
| author_sort | Ying Xu |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-0c138d58640f49f2bb1d88ce24a49e0a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-0c138d58640f49f2bb1d88ce24a49e0a2025-08-20T04:03:18ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-16368-9A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO modelYing Xu0Xinrong Ji1Zhengyang Zhu2State Grid Wuxi Power Supply CompanyState Grid Jiangsu Electric Power Co., LtdState Grid Nantong Power Supply CompanyAbstract 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.https://doi.org/10.1038/s41598-025-16368-9Photovoltaic power forecastingEnsemble empirical mode decomposition (EEMD)Long Short-Term memory (LSTM)XGBoostSnake optimization (SO)Hybrid model |
| spellingShingle | Ying Xu Xinrong Ji Zhengyang Zhu A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model Scientific Reports Photovoltaic power forecasting Ensemble empirical mode decomposition (EEMD) Long Short-Term memory (LSTM) XGBoost Snake optimization (SO) Hybrid model |
| title | A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model |
| title_full | A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model |
| title_fullStr | A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model |
| title_full_unstemmed | A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model |
| title_short | A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model |
| title_sort | photovoltaic power forecasting method based on the lstm xgboost eeda so model |
| topic | Photovoltaic power forecasting Ensemble empirical mode decomposition (EEMD) Long Short-Term memory (LSTM) XGBoost Snake optimization (SO) Hybrid model |
| url | https://doi.org/10.1038/s41598-025-16368-9 |
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