Assessing wind energy exploitation potential in several regions of Viet Nam using Kernel density estimation model
Chau T.T. Nguyen T.N. Do T.D.
25 October 2024Can Tho University
CTU Journal of Innovation and Sustainable Development
2024#16Issue Special issue25 - 34 pp.
This article analyzes and assesses the potential for wind energy exploitation in six regions of Viet Nam. The wind speed data are used to construct wind speed probability distributions (WSPDs) based on kernel density estimation (KDE). The KDE distribution, with six bandwidth selection methods, is implemented to generate probability density functions (PDFs) for each regions data to describe wind speed characteristics. The statistical tests Cramér-Von Mises (CvM), Anderson-Darling (A-D), and Kolmogorov-Smirnov (K-S) are applied to evaluate the PDFs goodness-of-fit performance. The analysis results present the KDE distribution using the least-squares cross-validation (LSCV), and the Scott bandwidth selection method has outstanding fitting performance. Based on these PDF distributions, the wind turbine (WT) power curve is used to estimate and predict the amount of electricity that can be produced. This study also proposes a reliable method for wind power output planning based on wind speed that can be universally applied.
Bandwidth selection , kernel density estimation , non-parametric distribution , wind energy , wind speed distribution
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Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Faculty of Information Technology, University of Economics Ho Chi Minh City-Vinh Long Campus, Viet Nam
Department of Robotics and Mechatronics
Faculty of Information Technology
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