A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy

Khurram Mushtaq, Asim Waris, Runmin Zou, Uzma Shafique, Niaz B. Khan, M. Ijaz Khan, Mohammed Jameel and Muhammad Imran Khan

Energy, 2024, vol. 304, issue C

Abstract: This research presents a comprehensive approach to improving the accuracy of wind turbine power curve (WTPC) modeling. The WTPC is a critical tool for monitoring wind turbine performance and estimating wind power potential, but current models have limited ability to capture the complex relationship between wind speed and power output. To address these limitations, this study implements a dual-pronged refinement of WTPC modeling. First, an innovative data preprocessing technique is introduced, using a 97 % confidence interval around a KNN-estimated WTPC constructed using the Laplace distribution to meticulously eliminate prominent outliers. Second, a novel WTPC modeling approach based on quantile regression is adopted, accounting for the asymmetric error characteristics in the loss function. Four distinct quantile regression models are developed, including three tree-based algorithms - decision trees, random forests, and gradient boosting - and a deep learning-based quantile regression neural network. Comparative analysis against ten established parametric and nonparametric techniques confirms the superiority of the proposed models, with the decision tree quantile regression model achieving the lowest validation errors. The proposed techniques are validated on two real-world datasets from operational wind turbines in Turkey and China, demonstrating significant improvements in WTPC modeling accuracy compared to conventional methods. Overall, this study successfully presents a comprehensive modeling approach that addresses outliers and leverages quantile regression to significantly enhance WTPC accuracy.

Keywords: Deep learning; Quantile regression; Quantile regression neural network; Regression trees; Wind energy; Wind turbine power curve modeling (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:304:y:2024:i:c:s0360544224017547

DOI: 10.1016/j.energy.2024.131981

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