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Improving the Efficiency of Electricity Consumption by Applying Real-Time Fuzzy and Fractional Control

Author

Listed:
  • Alexandru G. Berciu

    (Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400014 Cluj-Napoca, Romania)

  • Eva H. Dulf

    (Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400014 Cluj-Napoca, Romania)

  • Dan D. Micu

    (Department of Electrotechnics and Measurements, Faculty of Electrical Engineering, Technical University of Cluj-Napoca, 400014 Cluj-Napoca, Romania)

Abstract

Using energy more efficiently is one of the easiest ways to save money, reduce greenhouse gas emissions, and meet growing energy demands. Electricity consumption control is an emergent topic worldwide. The passive house idea is not new, but it is still actual and is discussed by researchers. This paper brings to the reader’s attention the combined use of fuzzy and fractional control methods to increase the performance of electricity consumption control, taking into account the current challenges in the energy field, together with a method for the automatic definition of fuzzy rules. In comparison with the no-control case, a 20% reduction in consumption is achieved with the present proposal. In the case of another control method, a 15% reduction was possible using Shakeri’s team’s method. The simulation of the proposed passive house control proves that it could ensure efficient electricity consumption that can be translated into electricity cost savings between 10 and 50 percent.

Suggested Citation

  • Alexandru G. Berciu & Eva H. Dulf & Dan D. Micu, 2022. "Improving the Efficiency of Electricity Consumption by Applying Real-Time Fuzzy and Fractional Control," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3807-:d:943197
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    References listed on IDEAS

    as
    1. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    2. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    3. Shakeri, Mohammad & Shayestegan, Mohsen & Reza, S.M. Salim & Yahya, Iskandar & Bais, Badariah & Akhtaruzzaman, Md & Sopian, Kamaruzzaman & Amin, Nowshad, 2018. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source," Renewable Energy, Elsevier, vol. 125(C), pages 108-120.
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