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Methods of Training of Neural Networks for Short Term Load Forecasting in Smart Grids

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Book cover Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10448))

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Abstract

Modern systems of voltage control in distribution grids need load forecast. The paper describes forecasting methods and concludes that using of artificial neural networks for this problem is preferable. It shows that for the complex real networks particle swarm method is faster and more accurate than traditional back propagation method.

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References

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Authors and Affiliations

  1. Wroclaw University of Science and Technology, 50370, Wroclaw, Poland

    Robert Lis

  2. Moscow Power Engineering Institute, Moscow, 111115, Russia

    Artem Vanin & Anastasiia Kotelnikova

Authors
  1. Robert Lis
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  2. Artem Vanin
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  3. Anastasiia Kotelnikova
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Corresponding author

Correspondence to Robert Lis .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management , Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Lis, R., Vanin, A., Kotelnikova, A. (2017). Methods of Training of Neural Networks for Short Term Load Forecasting in Smart Grids. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-67074-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67073-7

  • Online ISBN: 978-3-319-67074-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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