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.
Highlights
Non-renewable energy sources such as fossil fuels and coal are reported to be rapidly depleting owing to the booming human population’s increased need for energy [1]
The results demonstrated that the Generalized Extreme Value (GEV) distribution provided the best-fitted model for the majority of the wind station, followed by the Kumaraswamy distribution
This study was conducted to perform an analysis of the wind speed characteristics and determine the most suitable distribution model for the wind speed data set in four wind stations in Johor
Summary
Non-renewable energy sources such as fossil fuels and coal are reported to be rapidly depleting owing to the booming human population’s increased need for energy [1]. An alternative source of energy consisted of wind, biomass, hydropower, and solar energy can help to sustain the availability of the non-renewable energy source and reduce the effect of global warming. Due to the highly unpredictable nature and the random variation of wind patterns, it is crucial to investigate the most appropriate distribution function that could represent the wind speed pattern in a certain area [3]. In this regard, various studies have reported towards the suitability of the wind speed distributions for modelling the actual data. The findings showed that two out of five distributions, namely Lognormal and Gamma distribution, were found to provide the best fitted model of the actual data
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