Modelling the Wind Speed Oscillation Dynamics

  • K. Asokan
  • K. Satheesh Kumar
Part of the Communications in Computer and Information Science book series (CCIS, volume 305)


We present a detailed nonlinear time series analysis of the daily mean wind speed data measured at COCHIN/WILLINGDON (Latitude: +9.950, Longitude: +76.267 degrees, Elevation: 3 metres) from 2000 to 2010 using tools of non-linear dynamics. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic indicating the possibility of accurate short term prediction. The chaotic behaviour of wind dynamics explains the presence of periodicities amidst random like fluctuations found in the wind speed data, which forced many researchers to model wind dynamics by stochastic models previously. While most of the chaotic systems reported in the literature are either confined to laboratories or theoretical models, this is another natural system showing chaotic behaviour.


Wind Speed Mutual Information Lyapunov Exponent Total Electron Content Chaotic Behaviour 
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  1. Hegger, R., Kantz, H., Schreiber, T.: Practical implementation of nonlinear time series methods:The TISEAN package. Chaos 9, 413–435 (1999)zbMATHCrossRefGoogle Scholar
  2. Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)zbMATHGoogle Scholar
  3. Kumar, K.S., George, B., Renuka, G., Kumar, C.V.A., Venugopal, C.: Analysis of the fluctuations of the total electron content (TEC) measured at Goose Bay using the tools of nonlinear methods. J. Geophys. Res. 109, A02308 (2004), doi:10.1029/2002JA009768Google Scholar
  4. Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., Yan, Z.: A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13, 915–920 (2009)CrossRefGoogle Scholar
  5. Martín, M., Cremades, L.V., Santabárbara, J.M.: Analysis and modelling of time series of surface wind speed and direction. Int. J. Climatol. 19, 197–209 (1999)CrossRefGoogle Scholar
  6. Ott, E.: Chaos in Dynamical Systems. Cambridge University Press, Cambridge (1993)zbMATHGoogle Scholar
  7. Ott, E., Sauer, T., Yorke, J.A.: Coping with Chaos. Wiley, New York (1994)zbMATHGoogle Scholar
  8. Pavlos, G.P., Athanasiu, M.A., Diamantidis, D., Rigas, A.G., Sarris, E.T.: Nonlinear analysis of magnetospheric data Part I. Geometric characteristic of the AE index time series and comparison with nonlinear surrogate data. Nonlinear Proc. Geophys. 6, 51–65 (1999)CrossRefGoogle Scholar
  9. Sfetsos, A.: A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21, 23–35 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • K. Asokan
    • 1
  • K. Satheesh Kumar
    • 2
  1. 1.Department of MathematicsCollege of EngineeringThiruvananthapuramIndia
  2. 2.Department of Futures StudiesUniversity of KeralaThiruvananthapuramIndia

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