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A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands

Author

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  • Robbert Claeys

    (EELab/Lemcko, Department of Electromechanical, Systems and Metal Engineering, Ghent University, 8500 Kortrijk, Belgium)

  • Hakim Azaioud

    (EELab/Lemcko, Department of Electromechanical, Systems and Metal Engineering, Ghent University, 8500 Kortrijk, Belgium)

  • Rémy Cleenwerck

    (EELab/Lemcko, Department of Electromechanical, Systems and Metal Engineering, Ghent University, 8500 Kortrijk, Belgium)

  • Jos Knockaert

    (EELab/Lemcko, Department of Electromechanical, Systems and Metal Engineering, Ghent University, 8500 Kortrijk, Belgium)

  • Jan Desmet

    (EELab/Lemcko, Department of Electromechanical, Systems and Metal Engineering, Ghent University, 8500 Kortrijk, Belgium)

Abstract

This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands.

Suggested Citation

  • Robbert Claeys & Hakim Azaioud & Rémy Cleenwerck & Jos Knockaert & Jan Desmet, 2020. "A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands," Energies, MDPI, vol. 14(1), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:139-:d:469944
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    References listed on IDEAS

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    1. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
    2. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    3. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    4. Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
    5. Jimyung Kang & Jee-Hyong Lee, 2015. "Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications," Energies, MDPI, vol. 8(10), pages 1-24, October.
    6. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
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    Cited by:

    1. Michał Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyła & Jarosław Szymańda & Przemysław Janik & Jacek Bieńkowski & Przemysław Prus, 2021. "A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements," Energies, MDPI, vol. 14(4), pages 1-14, February.
    2. Michał Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyła & Jarosław Szymańda & Przemysław Janik & Jacek Bieńkowski & Przemysław Prus, 2021. "A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data," Energies, MDPI, vol. 14(4), pages 1-13, February.

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