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Reservoir Computing Approaches Applied to Energy Management in Industry

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Engineering Applications of Neural Networks (EANN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1000))

Abstract

Echo-State Neural Networks represent a very efficient solution for modelling of dynamic systems, thanks to their particular structure, which allows faithful reproduction of the behavior of the system to model with a usually limited computational burden for a training phase. This aspect favors the deployment of Echo-State Neural networks in the industrial field. In this paper, a novel application of such approach is proposed for the modelling of industrial processes. The developed models are part of a complex system for optimizing the exploitation of process off-gases in an integrated steelwork. Two models are presented and discussed, where both shallow Echo-State Neural Networks and Deep Echo State Neural networks are applied. The achieved results are presented and discussed, by comparing advantages and drawbacks of both approaches.

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Acknowledgments

The work described in the present paper was developed within the project entitled “Optimization of the management of the process gases network within the integrated steelworks - GASNET” (Contract No. RFSR-CT-2015-00029) and received funding from the Research Fund for Coal and Steel of the European Union, which is gratefully acknowledged. The sole responsibility of the issues treated in the present paper lies with the authors; the Union is not responsible for any use that may be made of the in-formation contained therein.

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Authors and Affiliations

  1. TeCIP Institute, ICT-COISP Center, Scuola Superiore Sant’Anna, Pisa, Italy

    Valentina Colla, Ismael Matino, Stefano Dettori, Silvia Cateni & Ruben Matino

Authors
  1. Valentina Colla
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  2. Ismael Matino
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  3. Stefano Dettori
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  4. Silvia Cateni
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  5. Ruben Matino
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Corresponding author

Correspondence to Valentina Colla .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John Macintyre

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Oxford Brookes University, Oxford, UK

    Chrisina Jayne

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Colla, V., Matino, I., Dettori, S., Cateni, S., Matino, R. (2019). Reservoir Computing Approaches Applied to Energy Management in Industry. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-20257-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20256-9

  • Online ISBN: 978-3-030-20257-6

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