Analysis of Wind Potential as a Form of New and Renewable Energy at Manna in Bengkulu
Abstract
The energy from fossil fuels is declining due to modern society's high energy demands. This study investigates the potential of renewable energy, focusing on wind energy as a sustainable solution. It analyzes the Weibull and Rayleigh distributions to assess wind speed suitability in Bengkulu. The average wind speed at Manna Beach is 2.5 m/s, with a minimum of 0.01 m/s and a maximum of 10.01 m/s. Data validation tests were conducted at a height of 10 meters using Chi-Square and Kolmogorov-Smirnov methods. The chi-square test results indicated the values of 0.11 for Weibull and 0.42 for Rayleigh. The Kolmogorov-Smirnov test yielded value of 0.82 for Weibull and 0.84 for Rayleigh. Therefore, the suggesting Weibull was more suitable. Furthermore, using the Aventa AV-7 wind turbine, an annual output of 11,096.29 kWh can supply 30% of the electricity demand, which requiring 199 turbines. Ultimately, 40 turbines effectively contribute 6% or 440,621.94 kWh of total demand.
##Keywords:##
Renewable energy, Wind energy, Weibull distribution, Rayleigh distribution, Wind turbine.
Published
Mar 30, 2025
How to Cite
PANGGABEAN, Yehezkiel et al.
Analysis of Wind Potential as a Form of New and Renewable Energy at Manna in Bengkulu.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 69, n. 1, p. 49-56, mar. 2025.
ISSN 2527-6085.
Available at: <https://isomase.org/Journals/index.php/jomase/article/view/528>. Date accessed: 08 may 2026.
doi: http://dx.doi.org/10.36842/jomase.v69i1.528.
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[4] Handoko, H., Adianto, A. & Loon, S.C. (2020). The effect of population behavior on new renewable energy in primary energy mix for 2025 national target: sumedang regency review, west java. Journal of Ocean, Mechanical and Aerospace-science and engineering-, 64(1), 1-8.
[5] Triatmoko, D., Arsyad, M. & Palloan, P. (2024). Assessment of wind energy potential in kolaka using era5-land reanalysis data. Advances In Social Humanities Research, 2(9), 1071-1088.
[6] Maulana, R. & Syafii, S. (2024). A systematic review of optimal power flow studies with renewable energy sources penetration. Journal of Ocean, Mechanical and Aerospace-science and engineering-, 68(1), 24-32.
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[11] Fathurridho Hermanto, M., Nandalianadhira, N., Suci, E., Wulandari, P. & Hutajulu, D. (2024). Western Lampung offshore wind and wave data correlation analysis. Original Article Journal of Science and Applicative Technology, 8(2), 125–131.
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[14] Widiyanto, W. (2013). Analisis probabilitas kecepatan angin untuk pesisir cilacap dengan menerapkan distribusi weibull dan rayleigh probability analisis of wind speed for cilacap coast by applying weibull and rayleigh distribution. 9(1).
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[16] Yendra, R., Marizal, M., Sawitri, W.I., Desvina, A.P. & Rahmadeni (2018). Distribusi_Weibull_Vs_Distribusi_Rayleig. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI-10).
[17] Alzubaidi, M., Hasan, K.N. & Meegahapola, L. (2020). Identification of suitable probability density function for wind speed profiles in power system studies. In 2020 Australasian Universities Power Engineering Conference (AUPEC) (pp. 1-6). IEEE.
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[19] Dokur, E. & Kurban, M. (2015). Wind speed potential analysis based on Weibull distribution. Balkan Journal of Electrical and Computer Engineering, 3, 231-235.
[20] Risdianto, D., Hesty, N.W., Zaky, T., Wijayanto, R.P., Mayasari, A.P. & Witjakso, A. (2024). Small-scale wind turbine selection based on wind energy potential analysis using windographer. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 13(4), 290-296.
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[22] PT. PLN (2024). Statistik PLN. https://web.pln.co.id/.
[23] Badan Pusat Statistik (2023). Badan Pusat Statistik Provinsi Bengkulu. Badan Pusat Statistik.
