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Matching Electricity Footprint of Commercial Customers to Industry-Specific Profiles for Enhanced Power Management

Author

Listed:
  • Tomasz Zabkowski

    (Institute of Information Technology, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Krzysztof Gajowniczek

    (Institute of Information Technology, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Jacek Brożyna

    (Department of Quantitative Methods, The Faculty of Management, Rzeszow University of Technology, Aleja Powstańców Warszawy 10/S, 35-959 Rzeszow, Poland)

  • Grzegorz Matejko

    (Polskie Towarzystwo Cyfrowe, Krakowskie Przedmieście 57/4, 20-076 Lublin, Poland)

Abstract

This paper presents a method for assigning the electricity consumption profiles of 10,129 commercial customers in Poland to specific industry profiles. The customer consumption data were compared with eight industry profiles for which the business activities were known. Additionally, a clustering analysis was conducted to identify homogeneous groups among the customers. The aim of this research was to develop a simple yet reliable approach for matching electricity usage patterns with specific profiles while relating them to the overall profile of the Polish power system. This offers valuable insights into how commercial customers utilize electricity and their actual contributions to the system’s peak load, which can further contribute to more efficient energy management and production planning within the Polish power system. Furthermore, the clustering analysis provides a new understanding of consumption patterns, allowing for better predictions of peak load behavior and more refined energy management strategies across sectors.

Suggested Citation

  • Tomasz Zabkowski & Krzysztof Gajowniczek & Jacek Brożyna & Grzegorz Matejko, 2024. "Matching Electricity Footprint of Commercial Customers to Industry-Specific Profiles for Enhanced Power Management," Energies, MDPI, vol. 17(23), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6120-:d:1536968
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    References listed on IDEAS

    as
    1. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    2. Faruqui, Ahmad & Hledik, Ryan & Newell, Sam & Pfeifenberger, Hannes, 2007. "The Power of 5 Percent," The Electricity Journal, Elsevier, vol. 20(8), pages 68-77, October.
    3. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    4. Tomasz Ząbkowski & Krzysztof Gajowniczek & Grzegorz Matejko & Jacek Brożyna & Grzegorz Mentel & Małgorzata Charytanowicz & Jolanta Jarnicka & Anna Olwert & Weronika Radziszewska & Jörg Verstraete, 2023. "Cluster-Based Approach to Estimate Demand in the Polish Power System Using Commercial Customers’ Data," Energies, MDPI, vol. 16(24), pages 1-21, December.
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