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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-16368-9 |
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