Wind Energy Science
Wind Energy Science
WES
Articles | Volume 8, issue 8
Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1277-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-1277-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 8
Research article
 | 
17 Aug 2023
Research article |  | 17 Aug 2023

Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions

Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed

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Cited articles

Ahmed, M. R.: Sample Data for Pentecost, Rakiraki and Rarotonga (Appendix 3), http://repository.usp.ac.fj/id/eprint/14126 (last access: 14 August 2023), 2016. 
Aukitino, T., Khan, M. G. M, and Ahmed, M. R.: Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters, Energ. Convers. Manage., 151, 641–660, https://doi.org/10.1016/j.enconman.2017.09.027, 2017. 
Azad, A. K., Rasul, M. G., and Yusaf, T.: Statistical diagnosis of the Best Weibull methods for wind power assessment for agricultural applications, Energies, 7, 3056–3085, https://doi.org/10.3390/en7053056, 2014. 
Carta, J. A., Ramirez, P., and Velazquez, S.: A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands, Renewable and sustainable energy reviews, 13, 933–955, https://doi.org/10.1016/j.rser.2008.05.005, 2009. 
Casella, G. and Berger, R. L.: Statistical inference, 2nd edn., Cengage Learning, ISBN 978-0-534-24312-8, 2020. 
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A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
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