Comparison of eight methods of Weibull distribution for determining the best-fit distribution parameters with wind data measured from the met-mast

In order to assess the wind characteristics of a specified region, a pre-analysis of the region can be made with different numerical methods. For instance, the two-parameter Weibull distribution is widely used in wind energy studies and the wind energy sector to obtain information about the wind characteristics of the specified region. The main goal of this study is to perform a detailed analysis of the data obtained from the wind measurement sensors on a meteorological mast with a height of 80 m to determine the wind characteristics and wind energy potential of a region in Osmaniye, Turkey. The suitability of the two-parameter Weibull distribution, which is the most popular probability distribution model, was investigated to evaluate the distribution of these wind data. In the precise determination of the Weibull distribution parameters (k and c), the suitability of eight different numerical methods, namely, graphical (GM), empirical of Justus (EMJ), empirical of Lysen (EML), power density (PDM), moment (MoM), maximum likelihood (MLM), modified maximum likelihood (MMLM), and alternative maximum likelihood (AMLM) methods, was examined. Root-mean-square error (RMSE), chi-square (X-2), and analysis of variance (R-2) were used to compare and verify the performance of these models. The best and worst performances in these eight methods were MMLM and GM, compared with the actual measured data. Also, wind power density was calculated considering these methods and prevailing wind directions.

Automated summary

This study analyzes the wind measurement data in Osmaniye, Turkey to determine the wind characteristics and wind energy potential of the region. The suitability of the two-parameter Weibull distribution model is evaluated, and it was found that the modified maximum likelihood method is the most reliable. The graphical method performs the worst in comparison.