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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0268894 |
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| author | Xing Liu Xingyu Mou Ying Zhang Hui Chen Xin Wang Xihao Sun Chengcheng Wang Ruixiang Shong Yunfeng Li |
| author_facet | Xing Liu Xingyu Mou Ying Zhang Hui Chen Xin Wang Xihao Sun Chengcheng Wang Ruixiang Shong Yunfeng Li |
| author_sort | Xing Liu |
| collection | DOAJ |
| description | 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-dimensional feature gain XGBoost model. First, traditional meteorological data are transformed into information entropy from an informational perspective, enabling the capture of randomness and nonlinear relationships within the data. Next, an entropy-driven high-dimensional (EDHD) classification feature selection method is designed to identify the relationships between covariates and response variables in the information entropy. Subsequently, a feature-gain XGBoost model is constructed to dynamically determine the optimal splitting features and thresholds, achieving the entropy-driven high-dimensional feature gain XGBoost model (EDHD-FG-XGBoost). Finally, experiments using real wind farm data from the northeastern region of China demonstrate the feasibility and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-40ba84142dd44294a2c608ad5aca8554 |
| institution | DOAJ |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-40ba84142dd44294a2c608ad5aca85542025-08-20T03:14:59ZengAIP Publishing LLCAIP Advances2158-32262025-06-01156065022065022-1210.1063/5.0268894EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and applicationXing Liu0Xingyu Mou1Ying Zhang2Hui Chen3Xin Wang4Xihao Sun5Chengcheng Wang6Ruixiang Shong7Yunfeng Li8National University of Singapore, 21 Lower Kent Ridge Rd., Singapore 119077Jilin Province Meteorological Information Network Center, Jilin Province, Changchun 130062, ChinaJilin Province Meteorological Information Network Center, Jilin Province, Changchun 130062, ChinaJilin Province Meteorological Information Network Center, Jilin Province, Changchun 130062, ChinaJilin Province Forestry Information Center, Jilin Province, Changchun 130022, ChinaDalian Ocean University, Liaoning, Dalian 116023, ChinaJilin Province Meteorological Information Network Center, Jilin Province, Changchun 130062, ChinaJilin Province Meteorological Information Network Center, Jilin Province, Changchun 130062, ChinaJilin Province Meteorological Information Network Center, Jilin Province, Changchun 130062, ChinaWind 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-dimensional feature gain XGBoost model. First, traditional meteorological data are transformed into information entropy from an informational perspective, enabling the capture of randomness and nonlinear relationships within the data. Next, an entropy-driven high-dimensional (EDHD) classification feature selection method is designed to identify the relationships between covariates and response variables in the information entropy. Subsequently, a feature-gain XGBoost model is constructed to dynamically determine the optimal splitting features and thresholds, achieving the entropy-driven high-dimensional feature gain XGBoost model (EDHD-FG-XGBoost). Finally, experiments using real wind farm data from the northeastern region of China demonstrate the feasibility and effectiveness of the proposed method.http://dx.doi.org/10.1063/5.0268894 |
| spellingShingle | Xing Liu Xingyu Mou Ying Zhang Hui Chen Xin Wang Xihao Sun Chengcheng Wang Ruixiang Shong Yunfeng Li EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application AIP Advances |
| title | EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application |
| title_full | EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application |
| title_fullStr | EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application |
| title_full_unstemmed | EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application |
| title_short | EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application |
| title_sort | edhd fg xgboost entropy driven high dimensional feature gain xgboost model for short term wind power prediction and application |
| url | http://dx.doi.org/10.1063/5.0268894 |
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