Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds

Musaed Alrashidi, Saifur Rahman and Manisa Pipattanasomporn

Renewable Energy, 2020, vol. 149, issue C, 664-681

Abstract: An accurate analysis of wind speeds is vital to justify wind energy projects. Statistical distributions can be used to characterize wind speeds through considering uncertainty in wind resources. However, the selection of the most suitable probability density function (PDF) is still a challenging task. Therefore, this study aims at developing a framework to accurately evaluate the performance of different PDFs to fit wind speeds, as well as presenting a new metaheuristic optimization algorithm method, called Social Spider Optimization (SSO), for wind characterization purposes. Seven sites in Saudi Arabia are used as case studies. Results indicate that combined PDFs outperform single PDFs in representing the observed wind speeds frequencies at all considered sites. Weibull distribution appears to be the most prevalent single distribution while no combined PDF dominates the others. In addition, the proposed SSO method is found to be the most efficient method for estimating PDFs parameters in Saudi Arabia. Overall, this proposed framework can be used to evaluate different wind PDFs in other countries.

Keywords: Wind speed; Probability density function; Combined density function; Metaheuristic optimization algorithm; Social spider optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119319202
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:149:y:2020:i:c:p:664-681

DOI: 10.1016/j.renene.2019.12.048

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().