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Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering

Author

Listed:
  • Vadim Borisov

    (Department of Computer Engineering, National Research University “Moscow Power Engineering Institute” (Smolensk Branch), 214013 Smolensk, Russia)

  • Maksim Dli

    (Department of Information Technology in Economics and Management, National Research University “Moscow Power Engineering Institute” (Smolensk Branch), 214013 Smolensk, Russia)

  • Artem Vasiliev

    (Rector, Moscow University for Industry and Finance “Synergy”, 129090 Moscow, Russia)

  • Yaroslav Fedulov

    (Department of Computer Engineering, National Research University “Moscow Power Engineering Institute” (Smolensk Branch), 214013 Smolensk, Russia)

  • Elena Kirillova

    (Department of Information Technology in Economics and Management, National Research University “Moscow Power Engineering Institute” (Smolensk Branch), 214013 Smolensk, Russia)

  • Nikolay Kulyasov

    (Higher School of Tariff Regulation, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

Abstract

A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.

Suggested Citation

  • Vadim Borisov & Maksim Dli & Artem Vasiliev & Yaroslav Fedulov & Elena Kirillova & Nikolay Kulyasov, 2021. "Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering," Energies, MDPI, vol. 14(18), pages 1-11, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5848-:d:636267
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    References listed on IDEAS

    as
    1. Martin, Ben R., 2012. "The evolution of science policy and innovation studies," Research Policy, Elsevier, vol. 41(7), pages 1219-1239.
    2. Geels, Frank W., 2010. "Ontologies, socio-technical transitions (to sustainability), and the multi-level perspective," Research Policy, Elsevier, vol. 39(4), pages 495-510, May.
    3. Susana Borrás & Jakob Edler (ed.), 2014. "The Governance of Socio-Technical Systems," Books, Edward Elgar Publishing, number 16034.
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