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

Abstract

: Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work on technologies 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. Keywords: Maximum Likelihood Estimation, Wind Speed, Weibull, Gamma, Lognormal, AIC, BIC