EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application

EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application

Wind power prediction is critical for the efficient utilization of wind energy and the stable operation of power grids. However, existing prediction methods struggle to handle the complex characteristics of meteorological data. To address this challenge, this paper proposes an entropy-driven high-di...

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Bibliographic Details
Main Authors: Xing Liu, Xingyu Mou, Ying Zhang, Hui Chen, Xin Wang, Xihao Sun, Chengcheng Wang, Ruixiang Shong, Yunfeng Li
Format: Article
Language:English
Published: AIP Publishing LLC 2025-06-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/5.0268894
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