Related Experiment Video
Updated: Aug 29, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
Published on: February 13, 2018
Comparison of eight methods of Weibull distribution for determining the best-fit distribution parameters with wind
Bulent Yaniktepe1, Osman Kara2, Ilyas Aladag3
1Energy Systems Engineering Department, Osmaniye Korkut Ata University, Osmaniye, Turkey. byaniktepe@osmaniye.edu.tr.
Abstract:
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 (X2), and analysis of variance (R2) 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.
Related Concept Videos
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Design Example: Calculating Safe Diameter for Wind-Exposed Disc
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
Maxwell-Boltzmann Distribution: Problem Solving
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Expected Frequencies in Goodness-of-Fit Tests
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

