Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application
ABSTRACT Wind speed variables play an important role in exploiting wind power. However, they are fluctuating and random. Therefore, understanding their characteristics and properties is necessary to improve exploitation efficiency. This research investigates various wind speed distribution models, b...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Wind Energy |
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| Online Access: | https://doi.org/10.1002/we.2970 |
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| _version_ | 1832581077008384000 |
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| author | Tin Trung Chau Thu Thi Hoai Nguyen Linh Nguyen Ton Duc Do |
| author_facet | Tin Trung Chau Thu Thi Hoai Nguyen Linh Nguyen Ton Duc Do |
| author_sort | Tin Trung Chau |
| collection | DOAJ |
| description | ABSTRACT Wind speed variables play an important role in exploiting wind power. However, they are fluctuating and random. Therefore, understanding their characteristics and properties is necessary to improve exploitation efficiency. This research investigates various wind speed distribution models, both parametric and nonparametric, to estimate wind speed probability density (WSPD). The distribution models are implemented on various wind speed datasets with distribution characteristics of varying complexity. The assessment of goodness of fit includes statistical tests including Cramér‐von Mises (CvM), Anderson‐Darling (A‐D), Kolmogorov‐Smirnov (K‐S), and chi‐square (χ2), along with indices correlated as mean absolute percent error (MAPE). The study highlights that the adaptive bandwidth kernel density estimation (AKDE) distribution model based on the nearest neighbor estimation (NNE) has superior goodness of fit performance. Wind turbine power curves are applied to calculate and compare expected, distribution‐based, and empirical power output. In addition, the difference between the power output estimated from the AKDE distribution and the estimate from the empirical wind speed is almost zero, so this estimated power is reliable and can be used as a reference for planning or evaluating wind farm efficiency. |
| format | Article |
| id | doaj-art-da60cf4186fe4e89ab65b02f9adeaf84 |
| institution | Kabale University |
| issn | 1095-4244 1099-1824 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Wind Energy |
| spelling | doaj-art-da60cf4186fe4e89ab65b02f9adeaf842025-01-30T10:32:38ZengWileyWind Energy1095-42441099-18242025-02-01282n/an/a10.1002/we.2970Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm ApplicationTin Trung Chau0Thu Thi Hoai Nguyen1Linh Nguyen2Ton Duc Do3Department of Robotics and Mechatronics, School of Engineering and Digital Sciences Nazarbayev University Astana KazakhstanSchool of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi VietnamInstitute of Innovation, Science and Sustainability Federation University Australia Mount Helen Victoria AustraliaDepartment of Robotics and Mechatronics, School of Engineering and Digital Sciences Nazarbayev University Astana KazakhstanABSTRACT Wind speed variables play an important role in exploiting wind power. However, they are fluctuating and random. Therefore, understanding their characteristics and properties is necessary to improve exploitation efficiency. This research investigates various wind speed distribution models, both parametric and nonparametric, to estimate wind speed probability density (WSPD). The distribution models are implemented on various wind speed datasets with distribution characteristics of varying complexity. The assessment of goodness of fit includes statistical tests including Cramér‐von Mises (CvM), Anderson‐Darling (A‐D), Kolmogorov‐Smirnov (K‐S), and chi‐square (χ2), along with indices correlated as mean absolute percent error (MAPE). The study highlights that the adaptive bandwidth kernel density estimation (AKDE) distribution model based on the nearest neighbor estimation (NNE) has superior goodness of fit performance. Wind turbine power curves are applied to calculate and compare expected, distribution‐based, and empirical power output. In addition, the difference between the power output estimated from the AKDE distribution and the estimate from the empirical wind speed is almost zero, so this estimated power is reliable and can be used as a reference for planning or evaluating wind farm efficiency.https://doi.org/10.1002/we.2970adaptive bandwidthkernel density estimationprobability density distributionwind energywind speed distribution |
| spellingShingle | Tin Trung Chau Thu Thi Hoai Nguyen Linh Nguyen Ton Duc Do Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application Wind Energy adaptive bandwidth kernel density estimation probability density distribution wind energy wind speed distribution |
| title | Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application |
| title_full | Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application |
| title_fullStr | Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application |
| title_full_unstemmed | Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application |
| title_short | Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application |
| title_sort | wind speed probability distribution based on adaptive bandwidth kernel density estimation model for wind farm application |
| topic | adaptive bandwidth kernel density estimation probability density distribution wind energy wind speed distribution |
| url | https://doi.org/10.1002/we.2970 |
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