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Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean

Author

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  • Akdag, S.A.
  • Bagiorgas, H.S.
  • Mihalakakou, G.

Abstract

The statistical characteristics of wind speed data recorded at nine buoys, located in Ionian and Aegean Sea (Eastern Mediterranean) are analyzed in this paper, in order to present a more accurate method for estimation of wind speed characteristics, according to the suitability of the probability distribution functions (pdf). This article has focussed on wind regimes that present nearly zero percentages of null wind speeds. The selected distributions for examination are the typical two-parameter Weibull wind speed distribution (W-pdf) and the two-component mixture Weibull distribution (WW-pdf), involving five parameters (two shape parameters, two scale parameters, and one proportionality parameter). Suitable software, based on the maximum likelihood method, is used in order to estimate the aforementioned two-parameters of the typical W-pdf and the five parameters of the mixed WW-pdf. The suitability of the aforementioned distributions is judged from the coefficient of determination (R2) and the fit standard error (RMSE) tests, which had been carried out between each one of the theoretical distributions and the corresponding experimental cumulative frequencies of the nine selected sites. From these tests it is clear that, in most cases (six experimental stations - having either unimodal or bimodal frequency distributions), mixed-Weibull distribution provides the highest degree of fit. In the other three cases, the mixing weight p of the two-component mixed Weibull density function equals to zero (p = 0), so the mixed-Weibull distribution is been transformed to the typical Simple-Weibull distribution. Hence, the general conclusion is that the aforementioned mixture of two Weibull distributions is more suitable for the description of such wind conditions and could offer less relative errors in determining the annual mean wind power density.

Suggested Citation

  • Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
  • Handle: RePEc:eee:appene:v:87:y:2010:i:8:p:2566-2573
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    References listed on IDEAS

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    1. Borhan, Y., 1998. "Mesoscale interactions on wind energy potential in the northern Aegean region: a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 2(4), pages 353-360, December.
    2. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    3. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    4. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    5. Celik, A.N., 2003. "Assessing the suitability of wind speed probabilty distribution functions based on wind power density," Renewable Energy, Elsevier, vol. 28(10), pages 1563-1574.
    6. Chang, Tsang-Jung & Tu, Yi-Long, 2007. "Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan," Renewable Energy, Elsevier, vol. 32(12), pages 1999-2010.
    7. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    8. İncecİk, S. & Erdoğmuş, F., 1995. "An investigation of the wind power potential on the western coast of Anatolia," Renewable Energy, Elsevier, vol. 6(7), pages 863-865.
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