Modelling Wind Speed Using Mixture Distributions in the Tangier Region

  • Fatima BahraouiEmail author
  • Hind Sefian
  • Zuhair Bahraoui
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 912)


The main objective of this research is to improve the predictability of wind generation, these include to propose a probabilistic prediction approach of the moment of appearance of these variations. In this perspective, we will compare the adjustment of wind speed distribution by Weibull distribution and two mixture distribution function as a solid alternative model to the eolian energy models. First with the mixture of the Weibull and Pareto distribution, and second with Lognormal and Pareto distribution. Our aim is to capture the outlier if there exists in the data and gives a most precise predictive estimation of the power density energy, evaluate the wind potential and predict the electrical energy produced in the site in order to size a wind farm on a site in Tangier while based on a judicious choice of wind turbines.


Wind speed Wind power density Wind variation Statistical analysis Weibull Lognormal Pareto Probability density function 


  1. 1.
    WEC: World energy scenarios. Technical report, World Energy Council (2016)Google Scholar
  2. 2.
    Ozay, C., Celiktas, M.S.: Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region. Energy Convers. Manag. 121, 49–54 (2016)CrossRefGoogle Scholar
  3. 3.
    Ali, S., Lee, S.-M., Jang, C.-M.: Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island e Incheon, South Korea. Renew. Energy 123, 652–663 (2018)CrossRefGoogle Scholar
  4. 4.
    Xiangyun, Q.: Statistical analysis of wind energy characteristics in Santiago Island, Cape Verde. Renew. Energy 115, 448–461 (2018)CrossRefGoogle Scholar
  5. 5.
    Wais, P.: Two and three-parameter Weibull distribution in available wind Power analysis. Renew. Energy 103, 15–29 (2017)CrossRefGoogle Scholar
  6. 6.
    Soulouknga, M.H., Doka, S.Y., Revanna, N., Djongyang, N., Kofane, T.C.: Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution. Renew. Energy 121, 1–8 (2018)CrossRefGoogle Scholar
  7. 7.
    Pishgar-Komleh, S.H., Akram, A.: Evaluation of wind energy potential for different turbine models based on the wind speed data of Zabol region, Iran. Sustain. Energy Technol. Assess. 22, 34–40 (2017)Google Scholar
  8. 8.
    MertKantar, Y., Usta, U.: Analysis of the upper-truncated Weibull distribution for wind speed. Energy Convers. Manag. 96(15), 81–88 (2015)Google Scholar
  9. 9.
    Usta, U., Arik, I., Yenilmez, I., MertKantar, Y.: A new estimation approach based on moments for estimating Weibull parameters in wind power applications. Energy Convers. Manag. 164, 570–578 (2018)CrossRefGoogle Scholar
  10. 10.
    GülAkgül, F., Şenoğlu, B., Arslan, T.: An alternative distribution to Weibull for modelling the wind speed data: inverse Weibull distribution. Energy Convers. Manag. 114, 234–240 (2016)CrossRefGoogle Scholar
  11. 11.
    Cohen, A.C.: Maximum likelihood estimation in the Weibull distribution based on complete and on censored samples. Technometrics 7, 579–588 (1965)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Sciences and TechniquesTangierMorocco
  2. 2.University of Chouaib Doukali, ESTEl JadidaMorocco

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