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Fractal characteristics in wind speed time series (WSTS) observed at Nalohou (Northern Benin)

Author Affiliations

  • 1Laboratory of Physics of Radiation (LPR), Abomey-Calavi University, BP: 526 UAC, Bénin
  • 2International Chair in Physics Mathematics and Applications (CIPMA-Chair Unesco), Abomey-Calavi University, BP: 526 UAC, Bénin
  • 3Laboratory of Physics of Radiation (LPR), Abomey-Calavi University, BP: 526 UAC, Bénin and Institute of Mathematics and Physical Science (IMSP), Abomey-Calavi University, BP: 526 UAC, Bénin
  • 4Laboratory of Applied Hydrology (LHA), Abomey-Calavi University, BP: 526 UAC, Bénin
  • 5Laboratory of Physics of Radiation (LPR), Abomey-Calavi University, BP: 526 UAC, Bénin
  • 6Laboratory of Applied Hydrology (LHA), Abomey-Calavi University, BP: 526 UAC, Bénin

Res. J. Physical Sci., Volume 7, Issue (1), Pages 1-7, January,4 (2019)


Five-years series of thirty minutes average wind speed obtained from AMMA-CATCH stations at Nalohou (Northern Benin), have been analyzed using fractal approach to determine the scaling behavior in wind speed. Wind Speed Time Series (WSTS) have been transferred into an appropriate data form: the fractal-Dimension (Df), and the Critical temporal Scale (Cts) are plotted as function of threshold (Th). Two invariance regimes are obtained in the WSTS. The first regime is defined from 30 min to 32h and the second is from 32 h to 43 days. The fractal Dimensions of these regimes are respectively in [0.2, 1] and [0.6, 1]. The critical temporal scale increases with the increased values of the threshold. Thus, the higher wind intensity can be observed necessary with a larger time scale. The fractal Dimension decreases when the threshold wind speed level increases indicating the presence of multifractal characteristics in the WSTS. This result is confirmed by the K(q)-q plots function analysis.


  1. Chang T.P., Ko H., Liu F.J., Chen P.H., Chang Y.P., Liang Y.H., Jang H.Y., Lin T.C. and Chen Y.H. (2012)., Fractal dimension of wind speed time series., Applied Energy, 93, 742-749.
  2. de Araujo Lima L. and Bezerra Filho C.R. (2010)., Wind energy assessment and wind farm simulation in Triunfo-Pernambuco, Brazil., Renewable Energy, 35(12), 2705-2713.
  3. Pimenta F., Kempton W. and Garvine R. (2008)., Combining meteorological stations and satellite data to evaluate the offshore wind power resource of Southeastern Brazil., Renewable Energy, 33(11), 2375-2387.
  4. Shipkovs P., Bezrukov V., Pugachev V., Bezrukovs V. and Silutins V. (2013)., Research of the wind energy resource distribution in the Baltic region., Renewable Energy, 49, 119-123.
  5. Leahy P.G. and Mckeogh E.J. (2013)., Persistence of low wind speed conditions and implications for wind power variability., Wind Energy, 16(4), 575-586.
  6. Bakker A.M.R. and Van Den Hurk B.J.J.M. (2012)., Estimation of persistence and trends in geostrophic wind speed for the assessment of wind supply in Northwest Europe., Climate Dynamics, 39(3-4), 767-782.
  7. Awanou C.N., Degbey J.M. and Ahlonsou E. (1991)., Estimation of the mean wind energy available in Benin (Ex Dahomey)., Renewable Energy, 1(5-6), 845-853.
  8. Houekpoheha M.A., Kounouhéwa B., Tokpohozin B.N. and Awanou C.N. (2014)., Estimation de la puissance énergétique éolienne à partir de la distribution de Weibull sur la c&te béninoise à Cotonou dans le Golfe de Guinée., Revue des Energies Renouvelables, 17(3), 489-495.
  9. Akpo A.B., Damada J.C.T., Donnou H.E.V., Kounouhewa B. and Awanou C.N. (2015)., Evaluation de la production énergétique d'un aérogénérateur sur un site isolé dans la région côtière du Bénin., Revue des Energies Renouvelables, 18(3), 457-468.
  10. Karakasidis T.E. and Charakopoulos A. (2009)., Detection of low-dimensional chaos in wind time series., Chaos, Solitons & Fractals, 41(4), 1723-1732.
  11. Jiménez-Hornero F.J., Pavón-Domínguez P., de Ravé E.G. and Ariza-Villaverde A.B. (2011)., Joint multifractal description of the relationship between wind patterns and land surface air temperature., Atmospheric Research, 99(3-4), 366-376.
  12. Lee C.K., Juang L.C., Wang C.C., Liao Y.Y., Yu C.C., Liu Y.C. and Ho D.S. (2006)., Scaling characteristics in ozone concentration time series (OCTS)., Chemosphere, 62(6), 934-946.
  13. Lee C.K., Ho D.S., Yu C.C. and Wang C.C. (2003)., Fractal analysis of temporal variation of air pollutant concentration by box counting., Environmental Modelling and Software, 18(3), 243-251.
  14. Haslett J. and Raftery A.E. (1989)., Space-time modeling with long-memory dependence: assessing Ireland's wind power resource., Applied Statistics, 38, 1-50.
  15. Mandelbrot B.B. (1983)., The Fractal Geometry of Nature., W.H. Freeman and Company, New York, 468.
  16. Lovejoy S.D.A.A., Schertzer D. and Tsonis A.A. (1987)., Functional box-counting and multiple elliptical dimensions in rain., Science, 235(4792), 1036-1038.
  17. Hubert P. and Carbonnel J.P. (1989)., Dimensions fractales de l'occurrence de ploy en climat soudano-sahélien., Hydrologie Continentale, 4(1), 3-10.
  18. Biaou A. (2004)., De la méso-échelle à la micro-échelle: Désagrégation spatio-temporelle multifractale des précipitations., Thèse de doctorat, Ecole de Mines de Paris, France.
  19. Hoang C.T. (2011)., Prise en compte des fluctuations spatio-temporelles pluies-débits pour une meilleure gestion de la ressource en eau et une meilleure évaluation des risques., Sciences de la Terre, Université Paris-Est.
  20. Lovejoy S. and Schertzer D. (1990)., Multifractals, universality classes and satellite and radar measurements of cloud and rain fields., J Geophys Res, 95, 2021-2034.
  21. Parisi G. and Frisch U. (1985)., Multifractal model of intermittency, in turbulence and predictability., geophysical fluid dynamics and climate dynamics, North Holland: 84-88.