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

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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Bibliographic Details
Main Authors:	Ying Xu, Xinrong Ji, Zhengyang Zhu
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-08-01
Series:	Scientific Reports
Subjects:	Photovoltaic power forecasting Ensemble empirical mode decomposition (EEMD) Long Short-Term memory (LSTM) XGBoost Snake optimization (SO) Hybrid model
Online Access:	https://doi.org/10.1038/s41598-025-16368-9
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