Data fitting distribution for wind speed in Mersing, Johor

Energy efficiency and renewable energy are the key to ensuring a secure, reliable, affordable, and sustainable energy system for a better future. One of the most suitable and environmentally beneficial types of renewable energy is wind energy. Due to the wind blows that are affected by the northeast...

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Bibliographic Details
Main Authors: Norrulashikin, Siti Mariam, Kamisan, Nur Arina Bazilah, Nor, Siti Rohani Mohd
Format: Conference Proceeding
Language:English
Subjects:
Alternative energy sources
Energy conversion
Energy harvesting
Extreme values
Goodness of fit
Monsoons
Profitability
Renewable energy
Renewable resources
Statistical analysis
Wind power
Wind power generation
Wind speed
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Summary:Energy efficiency and renewable energy are the key to ensuring a secure, reliable, affordable, and sustainable energy system for a better future. One of the most suitable and environmentally beneficial types of renewable energy is wind energy. Due to the wind blows that are affected by the northeast monsoon and southwest monsoon, Mersing, Johor is one of the locations in Malaysia that has the potential for wind power generation. But before implementing the use of wind speed for energy conversion, several considerations must be made to reduce resource waste and boost the profitability of various parties. In order to provide an accurate geographical assessment of wind energy potential for wind power harvesting, this study attempts to devise a statistical distribution that best matches the data. This study is significant because various places or stations have distinct wind data distributions, which will enable accurate computations of wind data dispersion. From January 1, 2002, to December 31, 2014, the Malaysian Meteorological Department provided statistics on wind speed. The five probability distributions that will be used for the Mersing station’s wind speed modelling are Weibull, Gamma, Lognormal, Generalized Extreme Value (GEV), and Rayleigh. The goodness-of-fit of each distribution was evaluated using the Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Chi-Squared ( χ 2 ) tests. Based on the results of KS, AD, and χ 2 test values at a 5% level of significance, it has been determined that the Generalized Extreme Value (GEV) distribution is the one that fits the wind speed data from Mersing wind stations the best. This finding was further validated by the use of Loglikelihood and the Akaike Information Criterion (AIC) value, which also revealed that the Generalized Extreme Value distribution best suited the wind speed data.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0192861