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Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks

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Green IT Engineering: Concepts, Models, Complex Systems Architectures

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 74))

Abstract

In this book chapter, we have considered a topical problem of intelligent energy management, which arises in the context of an intensively developed science direction—Green IT. The hybrid neuro-neo-fuzzy system and its high-speed learning algorithm are proposed. This system can be used for on-line prediction of essentially non-stationary nonlinear chaotic and stochastic time series, which describe electrical load producing and consuming processes. The considered hybrid adaptive system of computational intelligence has some advantages over the conventional artificial neural networks and neuro-fuzzy systems. The proposed hybrid neuro-neo-fuzzy prediction system provides a high quality load prediction that is very important for power systems.

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Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, 14 Nauky av., Kharkiv, 61166, Ukraine

    Yevgeniy Bodyanskiy, Olena Vynokurova & Iryna Pliss

  2. Lviv Politechnic National University, 12 S. Bandery st., Lviv, 79013, Ukraine

    Dmytro Peleshko

Authors
  1. Yevgeniy Bodyanskiy
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  2. Olena Vynokurova
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  3. Iryna Pliss
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  4. Dmytro Peleshko
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Corresponding author

Correspondence to Yevgeniy Bodyanskiy .

Editor information

Editors and Affiliations

  1. KhAI 503, Department of Computer Systems & Network KhAI 503, Kharkiv, Ukraine

    Vyacheslav Kharchenko

  2. PMBSSU, 68th, Dept of Intelligent Information Systems PMBSSU, 68th, Mykolaiv, Ukraine

    Yuriy Kondratenko

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory Sys Res Intit of Polish Acad of Science, Warszawa, Poland

    Janusz Kacprzyk

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Bodyanskiy, Y., Vynokurova, O., Pliss, I., Peleshko, D. (2017). Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-319-44162-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-44162-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44161-0

  • Online ISBN: 978-3-319-44162-7

  • eBook Packages: EngineeringEngineering (R0)

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