Comparative Analysis of Adaptive Filters for Predicting Wind-Power Generation (SLMS, NLMS, SGDLMS, WLMS, RLMS)

  • Ashima Arora
  • Rajesh Wadhvani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


Adaptive filters play an important role in prediction. This ability of adaptive filters have been successfully used in prediction of wind-power generation. This paper focuses on the comparison between adaptive filtering algorithms in order to determine which filter produces least error for predicting wind-power generation. Algorithms such as Standard least mean square (SLMS), Normalized least mean square (NL-MS), Weighted least mean square (WLMS), Stochastic Gradient Descent least mean square (SGDLMS), Recursive least Square (RLS) are implemented. The performance of the filters is evaluated using actual operational power data of a wind farm in America. Four performance criteria are used in the study of these algorithms: Mean Absolute Error, R-squared value, Computational Complexity, and Stability of the system.


Adaptive filtering algorithms Adaptive filter Computational complexity Least mean square Mean absolute error R-squared Wind power generation 


  1. 1.
    Ipakchi, A., Albuyeh, F.: Grid of the future. IEEE Power Energy Mag. 7(2), 52–62 (2009)CrossRefGoogle Scholar
  2. 2.
    Shokrzadeh, S., Jozani, M.J.: Wind turbine power curve modeling using advanced parametric & nonparametric methods. IEEE Trans. Sustain. Energy 5(4), 827–835 (2014)CrossRefGoogle Scholar
  3. 3.
    Gasch, R., Twele, J.: Wind Power Plants: Fundamentals, Design, Construction and Operation, pp. 46–113. Springer, Berlin (2012)CrossRefGoogle Scholar
  4. 4.
    Madisetti, V.K., Douglas, B.W.: Introduction to Adaptive Filters Digital Signal Processing Handbook, 2nd edn. CRC Press LLC, Boca Raton (2009). Chap. 18Google Scholar
  5. 5.
    Macchi, O.: Adaptive Processing: The Least Mean Squares Approach with Applications in Transmission, vol. 23, no. 11, pp. 45–78. Wiley, Chichester (1995)Google Scholar
  6. 6.
    Patil, A.P., Patil, M.R.: Computational complexity of adaptive algorithms in echo cancellation. SSRG Int. J. Electron. Commun. Eng. (SSRG-IJECE) 2(7), 16 (2015)Google Scholar
  7. 7.
    Clarkson, P.M.: Optimal and Adaptive Signal Processing. CRC Press, Boca Raton (1993)zbMATHGoogle Scholar
  8. 8.
    Dhiman, J., Ahmad, S., Gulia, K.: Comparison between adaptive filter algorithms (LMS, NLMS and RLS). Int. J. Sci. Eng. Technol. Res. (IJSETR) 2(5), 1100–1103 (2013)Google Scholar
  9. 9.
    Sharma, A., Juneja, Y.: Acoustic echo cancellation of from the signal using NLMS algorithm. Int. J. Res. Advent Technol. 2(6) (2014)Google Scholar
  10. 10.
    Shoval, D.J., Snelgrove, W.: Comparison of DC offset effects in four LMS adaptive algorithms. IEEE Trans. Circ. Syst. II Analog Digit. Sig. Process. 42(3), 176–185 (1995)Google Scholar
  11. 11.
    Zaknich, A.: Principles of Adaptive Filters and Self-learning Systems. Springer, London (2005)Google Scholar
  12. 12.
    Haykin, S.S.: Adaptive Filter Theory. Prentice Hall, Upper Saddle River (1996)zbMATHGoogle Scholar
  13. 13.
    Sayed, A.H., Kailath, T.: Recursive Least-Squares Adaptive Filters. Wiley, Los Angeles (2003)Google Scholar
  14. 14.
    Marshall, D.F., Jenkins, W.K., Murphy, J.J.: The use of orthogonal transforms for improving performance of adaptive filters. IEEE Trans. Circ. Syst. 36, 474–485 (1989)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Fushiki, T.: Estimation of prediction error by using K-fold cross-validation. Stat. Comput. 21(2), 137–146 (2011)MathSciNetCrossRefzbMATHGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and EngineeringMaulana Azad National Institute of TechnologyBhopalIndia

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