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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Frontiers in energy research Ročník 10
Hlavní autoři: Xiong, Xiong, Guo, Xiaojie, Zeng, Pingliang, Zou, Ruiling, Wang, Xiaolong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Frontiers Media S.A 10.05.2022
Témata:
Bayesian hyperparameters optimization
machine learning
numerical weather prediction
wind power forecasting
Xgboost algorithm
ISSN:2296-598X, 2296-598X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:2296-598X
2296-598X
DOI:10.3389/fenrg.2022.905155