Probability distributions of wind speed in the UAE

•We assessed the goodness-of-fit of different distributions to wind speed data in the UAE.•Performances of parametric, mixture and non-parametric distributions are compared.•Mixture of two-component Weibull distribution gives the overall best fit.•The Kappa and Generalized Gamma are the parametric d...

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Bibliographic Details
Published in:	Energy conversion and management Vol. 93; pp. 414 - 434
Main Authors:	Ouarda, T.B.M.J., Charron, C., Shin, J.-Y., Marpu, P.R., Al-Mandoos, A.H., Al-Tamimi, M.H., Ghedira, H., Al Hosary, T.N.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15-03-2015
Subjects:	Coefficient of determination Coefficients Density Kappa distribution Mathematical models Mixture distribution Model selection criteria Non-parametric model Probability density function Probability density functions Stations Statistics United Arab Emirates Wind energy Wind speed Wind speed distribution United Arab Emirates Model selection criteria Wind speed distribution Coefficient of determination Non-parametric model Kappa distribution Mixture distribution Probability density function
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Summary:	•We assessed the goodness-of-fit of different distributions to wind speed data in the UAE.•Performances of parametric, mixture and non-parametric distributions are compared.•Mixture of two-component Weibull distribution gives the overall best fit.•The Kappa and Generalized Gamma are the parametric distributions giving the best fit. For the evaluation of wind energy potential, probability density functions (pdfs) are usually used to describe wind speed distributions. The selection of the appropriate pdf reduces the wind power estimation error. The most widely used pdf for wind energy applications is the 2-parameter Weibull probability density function. In this study, a selection of pdfs are used to model hourly wind speed data recorded at 9 stations in the United Arab Emirates (UAE). Models used include parametric models, mixture models and one non-parametric model using the kernel density concept. A detailed comparison between these three approaches is carried out in the present work. The suitability of a distribution to fit the wind speed data is evaluated based on the log-likelihood, the coefficient of determination R2, the Chi-square statistic and the Kolmogorov–Smirnov statistic. Results indicate that, among the one-component parametric distributions, the Kappa and Generalized Gamma distributions provide generally the best fit to the wind speed data at all heights and for all stations. The Weibull was identified as the best 2-parameter distribution and performs better than some 3-parameter distributions such as the Generalized Extreme Value and 3-parameter Lognormal. For stations presenting a bimodal wind speed regime, mixture models or non-parametric models were found to be necessary to model adequately wind speeds. The two-component mixture distributions give a very good fit and are generally superior to non-parametric distributions.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0196-8904 1879-2227
DOI:	10.1016/j.enconman.2015.01.036