Modelling the wind energy potential at selected locations in Malaysia

• Published in: AIP Conference Proceedings
• Main Author: Mohamad N.A.; Zahari S.M.; Fadzil F.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188419719&doi=10.1063%2f5.0195433&partnerID=40&md5=34a6450fa23fdca5770c615a790a6f8b

In Malaysia, energy is mainly generated from fossil fuel sources. Land processing and waste storage and disposal in the fossil fuel industry have led to an increase in the level of carbon dioxide and worsened global warming pollution. Thus, the purpose of this study is to consider wind energy as a potential supply of renewable energy in addressing insufficient energy and pollution problems in Malaysia. First, wind speed data from 2017 to 2021 from the top three selected wind stations in Malaysia, Kudat, Mersing and Labuan were modelled and compared using the classic Weibull distribution and New Flexible Extended Weibull. The maximum likelihood estimation method was used to estimate the parameters for both distributions. Anderson-Darling's goodness of fit-test and cumulative distribution function were used to determine the best distribution. Three model selection criteria, namely AIC, BIC, and HQIC, were used to determine the appropriate wind speed distribution. The results show that Weibull distribution is the best fit for Kudat and Mersing wind data meanwhile New Flexible Extended Weibull distribution fits well with Labuan. The best distribution was then employed to estimate the wind power density of the three wind stations. Mersing shows the most promising wind station for future wind energy resources. The station is able to produce almost 40% of wind power from the prevailing wind speed during the first three months of every year as compared to Kudat and Labuan stations. Mersing also has the most stable wind variation with Weibull shape and scale parameters of 1.913 and 0.294 respectively. © 2024 Author(s).