Statistical Fitting of Wind Speed Data for Determination of Wind Power Potentials in Saudi Arabia

Al-Shehri, MA; Guo, Y; Lei, G

Statistical Fitting of Wind Speed Data for Determination of Wind Power Potentials in Saudi Arabia

Al-Shehri, MA Guo, Y

Lei, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 International Conference on Decision Aid Sciences and Application (DASA), 2021, 00, pp. 1021-1024
Issue Date:: 2021-01-15

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Field	Value	Language
dc.contributor.author	Al-Shehri, MA
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.date	2020-11-08
dc.date.accessioned	2022-06-29T03:19:54Z
dc.date.available	2022-06-29T03:19:54Z
dc.date.issued	2021-01-15
dc.identifier.citation	2020 International Conference on Decision Aid Sciences and Application (DASA), 2021, 00, pp. 1021-1024
dc.identifier.isbn	978-1-7281-9677-0
dc.identifier.uri	http://hdl.handle.net/10453/158454
dc.description.abstract	This paper present a statistical analysis of wind speed data for the Kingdom of Saudi Arabia. The study aim to determine the best probability distribution function for the wind speed data of some selected sites in the Kingdom. This is achieved by analysing a few parameter distribution functions. The probability distribution functions used in the analyses were Weibull, gamma, and Rayleigh distributions. In order to determine the best fits, a mean annual error was used. Results shows that the Weibull distribution has a 0.000601 annual average error, followed by the gamma distribution with an annual error of 0.362221, and finally, the Rayleigh distribution with an annual distribution error 0.63323. This indicates that the sites under study are suitable for electrical power generation in both grid-connected and off-grid modes. Comparing the annual error of each site shows that the gamma distribution performs better compared to other distributions. The results determined the best site for wind speed and very important for various microgrids designs in the Kingdom of Saudi Arabia.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 International Conference on Decision Aid Sciences and Application (DASA)
dc.relation.ispartof	2020 International Conference on Decision Aid Sciences and Application
dc.relation.isbasedon	10.1109/dasa51403.2020.9317209
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Statistical Fitting of Wind Speed Data for Determination of Wind Power Potentials in Saudi Arabia
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sakheer, Bahrain
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-29T03:19:53Z
pubs.finish-date	2020-11-09
pubs.publication-status	Published
pubs.start-date	2020-11-08
pubs.volume	00

Abstract:

This paper present a statistical analysis of wind speed data for the Kingdom of Saudi Arabia. The study aim to determine the best probability distribution function for the wind speed data of some selected sites in the Kingdom. This is achieved by analysing a few parameter distribution functions. The probability distribution functions used in the analyses were Weibull, gamma, and Rayleigh distributions. In order to determine the best fits, a mean annual error was used. Results shows that the Weibull distribution has a 0.000601 annual average error, followed by the gamma distribution with an annual error of 0.362221, and finally, the Rayleigh distribution with an annual distribution error 0.63323. This indicates that the sites under study are suitable for electrical power generation in both grid-connected and off-grid modes. Comparing the annual error of each site shows that the gamma distribution performs better compared to other distributions. The results determined the best site for wind speed and very important for various microgrids designs in the Kingdom of Saudi Arabia.

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http://hdl.handle.net/10453/158454