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

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

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.

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Authors and Affiliations

  1. Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, M.P., India

    Ashima Arora & Rajesh Wadhvani

Authors
  1. Ashima Arora
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  2. Rajesh Wadhvani
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Corresponding author

Correspondence to Ashima Arora .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Arora, A., Wadhvani, R. (2018). Comparative Analysis of Adaptive Filters for Predicting Wind-Power Generation (SLMS, NLMS, SGDLMS, WLMS, RLMS). In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_82

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_82

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

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