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

Okumu Otieno Kevin,Alilah Anekeya David,John Matuya,Edgar Otumba

doi:10.11648/j.ijsd.20200603.13

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.

Highlights

Wind speed distribution characteristics refer to the wind speed parameters like the mean, variance, standard deviation, and covariance
The investors and the wind industry needs to understand the wind speed characteristics of this region. This is because the wind speed is the most significant factor in the installation process of any wind plant
We considered a gamma distribution with shape parameter and scale parameter since it is the distribution that is widely used in real-life data sets

Summary

Introduction

Wind speed distribution characteristics refer to the wind speed parameters like the mean, variance, standard deviation, and covariance. There is a need to study the variations of these parameters for any given specific geographical site before installing the wind plant This is only possible if there is an approved statistical distribution that has been examined and recommended for the site or region of interest. The investors and the wind industry needs to understand the wind speed characteristics of this region This is because the wind speed is the most significant factor in the installation process of any wind plant. There is a need to have complete information about wind speed characteristics This can only be possible by having a recommended statistical distribution for examining the wind speed data. Leaving the wind industry and other investors with incomplete

Objectives

Methods

Results

Conclusion