Statistical analysis of wind speed data using Weibull distribution parameters

2014 ◽  
Author(s):  
Anurag Chauhan ◽  
R. P. Saini
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Wind Speed Data ◽  
Distribution Parameters

Calculation of Weibull Distribution parameters at low wind speed and performance analysis

2021 ◽  
pp. 1-24
Author(s):  
Yusuf Alper Kaplan
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Statistical Indicator ◽  
Low Wind Speed ◽  
Distribution Parameters ◽  
Wind Energy Potential ◽  
And Performance

In this study, the compatibility of the real wind energy potential to the estimated wind energy potential by Weibull Distribution Function (WDF) of a region with low average wind speed potential was examined. The main purpose of this study is to examine the performance of six different methods used to find the coefficients of the WDF and to determine the best performing method for selected region. In this study seven-year hourly wind speed data obtained from the general directorate of meteorology of this region was used. The root mean square error (RMSE) statistical indicator was used to compare the efficiency of all used methods. Another main purpose of this study is to observe the how the performance of the used methods changes over the years. The obtained results showed that the performances of the used methods showed slight changes over the years, but when evaluated in general, it was observed that all method showed acceptable performance. Based on the obtained results, when the seven-year data is evaluated in this selected region, it can be said that the MM method shows the best performance.

scholarly journals Fitting Wind Speed to a 3-Parameter Distribution Using Maximum Likelihood Technique

2021 ◽  
Author(s):  
Benedict Troon
Keyword(s):  
Maximum Likelihood ◽  
Wind Speed ◽  
Gamma Distribution ◽  
Model Comparison ◽  
Information Criterion ◽  
Parameter Distribution ◽  
Wind Speed Data ◽  
Distribution Parameters ◽  
Total Capacity ◽  
Maximum Likelihood Technique

Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work ontechnologies that can help in harnessing the wind with a vision of achieving a total capacity of 2GW of wind energy by 2030.The objective of this research is to find the best three-parameter wind speed distribution for examining wind speed using the maximum likelihood fitting technique. To achieve the objective, the study used hourly wind speed data collected for a period of three years (2016 – 2018) from five sites within Narok County. The study examines the best distributions that the data fits and then conducted a suitability test of the distributions using the Kolmogorov-Smirnov test. The distribution parameters were fitted using maximum likelihood technique and model comparison test conducted using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values with the decision rule that the best distribution relies on the distribution with the smaller AIC and BIC values. The research showed that the best distribution is the gamma distribution with the shape parameter of 2.071773, scale parameter of 1.120855, and threshold parameter of 0.1174. A conclusion that gamma distribution is the best three-parameter distribution for examining the Narok country wind speed data

scholarly journals Testing average wind speed using sampling plan for Weibull distribution under indeterminacy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Aslam
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Sample Size ◽  
Sampling Plan ◽  
Average Wind Speed ◽  
Wind Speed Data ◽  
Average Wind

AbstractThe time truncated plan for the Weibull distribution under the indeterminacy is presented. The plan parameters of the proposed plan are determined by fixing the indeterminacy parameter. The plan parameters are given for various values of indeterminacy parameters. From the results, it can be concluded that the values of sample size reduce as indeterminacy values increase. The application of the proposed plan is given using wind speed data. From the wind speed example, it is concluded that the proposed plan is helpful to test the average wind speed at smaller values of sample size as compared to existing sampling plan.

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