Accuracy testing of different methods for estimating weibull parameters of wind energy at various heights above sea level

Authors

Sajid Ali
Hongbae Park
Adnan Aslam Noon
Aamer Sharif, Edith Cowan UniversityFollow
Daeyong Lee

Document Type

Journal Article

Publication Title

Energies

Volume

17

Issue

9

Publisher

MDPI

School

School of Engineering

RAS ID

70400

Funders

Korea Institute of Energy Technology Evaluation and Planning

Grant Number

20214000000180, 20224000000220

Comments

Ali, S., Park, H., Noon, A. A., Sharif, A., & Lee, D. (2024). Accuracy testing of different methods for estimating weibull parameters of wind energy at various heights above sea level. Energies, 17(9), 2173. https://doi.org/10.3390/en17092173

Abstract

The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study’s findings can be used in various fields, e.g., wind energy forecasting and wind farm planning.

DOI

10.3390/en17092173

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