Analysis of wind speed characteristics using probability distribution in Johor

Abstract

Renewable energy and energy efficiency are the key factors to ensure a safe, reliable, affordable as well as sustainable energy system for a better future. One of the most congruous, environment-friendly, and renewable energy sources is wind energy. However, it is consequential to examine the suitable probability distribution function to study the wind speed characteristics before the element can be harnessed as a source of energy. In this study, five probability distributions, Gamma, Generalized Extreme Value (GEV), Lognormal, Rayleigh and Weibull distribution were selected to model the wind speed data from four wind stations in Johor in a ten-year period. In addition, the method of maximum likelihood estimation (MLE) was applied to obtain the parameter estimation for each selected distribution function, followed by the plotting the graphical representation of probability distribution function (PDF) and cumulative distribution function (CDF) for the theoretical distributions against the provided wind speed data. To determine the best-fitted model of the probability distribution, the Kolmogorov Smirnov (KS) test and Anderson Darling (AD) test were employed to assess the goodness-of-fit for each model distribution. Based on the plotted graph and calculated goodness-of-fit results, GEV distribution was found to be the best-fitted model for the wind speed dataset in Senai, Mersing, and Batu Pahat wind station, while Gamma distribution established the optimum model for the actual wind speed dataset in Kluang station.