A Short-Term Wind Power Forecast Method via XGBoost Hyper-Parameters Optimization
The improvement of wind power prediction accuracy is beneficial to the effective utilization of wind energy. An improved XGBoost algorithm via Bayesian hyperparameter optimization (BH-XGBoost method) was proposed in this article, which is employed to forecast the short-term wind power for wind farms...
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| Vydáno v: | Frontiers in energy research Ročník 10 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Frontiers Media S.A
10.05.2022
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| ISSN: | 2296-598X, 2296-598X |
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| Abstract | The improvement of wind power prediction accuracy is beneficial to the effective utilization of wind energy. An improved XGBoost algorithm
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Bayesian hyperparameter optimization (BH-XGBoost method) was proposed in this article, which is employed to forecast the short-term wind power for wind farms. Compared to the XGBoost, SVM, KELM, and LSTM, the results indicate that BH-XGBoost outperforms other methods in all the cases. The BH-XGBoost method could yield a more minor estimated error than the other methods, especially in the cases of wind ramp events caused by extreme weather conditions and low wind speed range. The comparison results led to the recommendation that the BH-XGBoost method is an effective method to forecast the short-term wind power for wind farms. |
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| AbstractList | The improvement of wind power prediction accuracy is beneficial to the effective utilization of wind energy. An improved XGBoost algorithm via Bayesian hyperparameter optimization (BH-XGBoost method) was proposed in this article, which is employed to forecast the short-term wind power for wind farms. Compared to the XGBoost, SVM, KELM, and LSTM, the results indicate that BH-XGBoost outperforms other methods in all the cases. The BH-XGBoost method could yield a more minor estimated error than the other methods, especially in the cases of wind ramp events caused by extreme weather conditions and low wind speed range. The comparison results led to the recommendation that the BH-XGBoost method is an effective method to forecast the short-term wind power for wind farms. The improvement of wind power prediction accuracy is beneficial to the effective utilization of wind energy. An improved XGBoost algorithm via Bayesian hyperparameter optimization (BH-XGBoost method) was proposed in this article, which is employed to forecast the short-term wind power for wind farms. Compared to the XGBoost, SVM, KELM, and LSTM, the results indicate that BH-XGBoost outperforms other methods in all the cases. The BH-XGBoost method could yield a more minor estimated error than the other methods, especially in the cases of wind ramp events caused by extreme weather conditions and low wind speed range. The comparison results led to the recommendation that the BH-XGBoost method is an effective method to forecast the short-term wind power for wind farms. |
| Author | Xiong, Xiong Zeng, Pingliang Wang, Xiaolong Guo, Xiaojie Zou, Ruiling |
| Author_xml | – sequence: 1 givenname: Xiong surname: Xiong fullname: Xiong, Xiong – sequence: 2 givenname: Xiaojie surname: Guo fullname: Guo, Xiaojie – sequence: 3 givenname: Pingliang surname: Zeng fullname: Zeng, Pingliang – sequence: 4 givenname: Ruiling surname: Zou fullname: Zou, Ruiling – sequence: 5 givenname: Xiaolong surname: Wang fullname: Wang, Xiaolong |
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