Fitting Wind Speed to a Two Parameter Distribution Model Using Maximum Likelihood Estimation Method

Abstract

Abstract: Kenya is among the countries that are continuously investing in wind energy to meet her electricity demand. Kenya is working towards its vision 2030 of achieving a total of 2GW of energy from wind industry. To achieve this, there is a need that all the relevant data on wind characteristics must be available. The purpose of this study is, therefore, to find the most efficient two-parameter model for fitting wind speed distribution for Narok County in Kenya, using the maximum likelihood method. Hourly wind speed data collected for a period of three years (2016 to 2018) from five sites within Narok County was used. Each of the distribution’s parameters was estimated and then a suitability test of the parameters was conducted using the goodness of fit test statistics, Kolmogorov-Smirnov, and Anderson-Darling. An efficiency test was determined using the Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, with the best decision taken based on the distribution having a smaller value of AIC and BIC. The results showed that the best distributions were the gamma distribution with the shape parameter of 2.47634 and scale parameter of 1.25991, implying that gamma distribution was the best distribution for modeling Narok County wind speed data. Keywords: Maximum Likelihood Estimation, Wind Speed, Weibull, Gamma, Lognormal