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Five different distributions and metaheuristics to model wind speed distribution

Year 2021, Volume: 7 Issue: Supp 14, 1898 - 1920, 30.12.2021
https://doi.org/10.18186/thermal.1051262

Abstract

This paper presents a comprehensive empirical study of five distribution functions to analyze wind energy potential: Rayleigh, Weibull, Gamma, Burr Type XII, and Generalized Extreme Value. In addition, two metaheuristics optimization methods, Grey Wolf optimization and Whale optimization algorithm, are utilized to determine the optimal parameter values of each distribution. Five error measures are investigated and compared to test the accuracy of the introduced distributions and optimization methods, such as mean absolute error, root mean square error, regression coefficient, correlation coefficient, and net fitness. The Catalca site in Istanbul, Turkey, was selected to be the case study to conduct this analysis. The obtained results confirm that all introduced distributions based on optimization methods efficiently model wind speed distribution in the selected site. Although Gamma distribution based on GWO and WOA outperformed other distributions for all datasets at all heights, it was the worst in terms of computation complexity. Rayleigh distribution occupied the latest rank, but it was the best in terms of computation complexity. MATLAB 2020b and Excel 365 were used to perform this study.

References

  • The article references can be accessed from the .pdf file.
Year 2021, Volume: 7 Issue: Supp 14, 1898 - 1920, 30.12.2021
https://doi.org/10.18186/thermal.1051262

Abstract

References

  • The article references can be accessed from the .pdf file.
There are 1 citations in total.

Details

Primary Language English
Subjects Thermodynamics and Statistical Physics
Journal Section Articles
Authors

Mohammed Wadı 0000-0001-8928-3729

Publication Date December 30, 2021
Submission Date February 6, 2021
Published in Issue Year 2021 Volume: 7 Issue: Supp 14

Cite

APA Wadı, M. (2021). Five different distributions and metaheuristics to model wind speed distribution. Journal of Thermal Engineering, 7(Supp 14), 1898-1920. https://doi.org/10.18186/thermal.1051262
AMA Wadı M. Five different distributions and metaheuristics to model wind speed distribution. Journal of Thermal Engineering. December 2021;7(Supp 14):1898-1920. doi:10.18186/thermal.1051262
Chicago Wadı, Mohammed. “Five Different Distributions and Metaheuristics to Model Wind Speed Distribution”. Journal of Thermal Engineering 7, no. Supp 14 (December 2021): 1898-1920. https://doi.org/10.18186/thermal.1051262.
EndNote Wadı M (December 1, 2021) Five different distributions and metaheuristics to model wind speed distribution. Journal of Thermal Engineering 7 Supp 14 1898–1920.
IEEE M. Wadı, “Five different distributions and metaheuristics to model wind speed distribution”, Journal of Thermal Engineering, vol. 7, no. Supp 14, pp. 1898–1920, 2021, doi: 10.18186/thermal.1051262.
ISNAD Wadı, Mohammed. “Five Different Distributions and Metaheuristics to Model Wind Speed Distribution”. Journal of Thermal Engineering 7/Supp 14 (December 2021), 1898-1920. https://doi.org/10.18186/thermal.1051262.
JAMA Wadı M. Five different distributions and metaheuristics to model wind speed distribution. Journal of Thermal Engineering. 2021;7:1898–1920.
MLA Wadı, Mohammed. “Five Different Distributions and Metaheuristics to Model Wind Speed Distribution”. Journal of Thermal Engineering, vol. 7, no. Supp 14, 2021, pp. 1898-20, doi:10.18186/thermal.1051262.
Vancouver Wadı M. Five different distributions and metaheuristics to model wind speed distribution. Journal of Thermal Engineering. 2021;7(Supp 14):1898-920.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK http://eds.yildiz.edu.tr/journal-of-thermal-engineering