AUTHOR=Zhou Kai , Han Hao , Li Junfen , Wang Yongjie , Tang Wei , Han Fei , Li Yulei , Bi Ruyu , Zhao Haitao , Jiao Lingxiao TITLE=Interval model of a wind turbine power curve JOURNAL=Frontiers in Energy Research VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1305612 DOI=10.3389/fenrg.2023.1305612 ISSN=2296-598X ABSTRACT=

The wind turbine power curve model is critical to a wind turbine’s power prediction and performance analysis. However, abnormal data in the training set decrease the prediction accuracy of trained models. This paper proposes a sample average approach-based method to construct an interval model of a wind turbine, which increases robustness against abnormal data and further improves the model accuracy. We compare our proposed methods with the traditional neural network-based and Bayesian neural network-based models in experimental data-based validations. Our model shows better performance in both accuracy and computational time.