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Data-driven load profiles and the dynamics of residential electricity consumption

Author

Listed:
  • Mehrnaz Anvari

    (Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association)

  • Elisavet Proedrou

    (DLR Institute for Networked Energy Systems)

  • Benjamin Schäfer

    (Queen Mary University of London
    Norwegian University of Life Sciences
    Institute for Automation and Applied Informatics, Karlsruhe Institute for Technology)

  • Christian Beck

    (Queen Mary University of London
    The Alan Turing Institute)

  • Holger Kantz

    (Max Planck Institute for the Physics of Complex Systems)

  • Marc Timme

    (Chair for Network Dynamics, Center for Advancing Electronics Dresden (cfaed) and Institute for Theoretical Physics, Technical University of Dresden
    Lakeside Labs)

Abstract

The dynamics of power consumption constitutes an essential building block for planning and operating sustainable energy systems. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of the variations in consumption dynamics is still missing. Here, we analyse highly resolved residential electricity consumption data of Austrian, German and UK households and propose a generally applicable data-driven load model. Specifically, we disentangle the average demand profiles from the demand fluctuations based purely on time series data. We introduce a stochastic model to quantitatively capture the highly intermittent demand fluctuations. Thereby, we offer a better understanding of demand dynamics, in particular its fluctuations, and provide general tools for disentangling mean demand and fluctuations for any given system, going beyond the standard load profile (SLP). Our insights on the demand dynamics may support planning and operating future-compliant (micro) grids in maintaining supply-demand balance.

Suggested Citation

  • Mehrnaz Anvari & Elisavet Proedrou & Benjamin Schäfer & Christian Beck & Holger Kantz & Marc Timme, 2022. "Data-driven load profiles and the dynamics of residential electricity consumption," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31942-9
    DOI: 10.1038/s41467-022-31942-9
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    References listed on IDEAS

    as
    1. Train, Kenneth & Herriges, Joseph & Windle, Robert, 1985. "Statistically adjusted engineering (SAE) models of end-use load curves," Energy, Elsevier, vol. 10(10), pages 1103-1111.
    2. Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
    3. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    4. Marszal-Pomianowska, Anna & Heiselberg, Per & Kalyanova Larsen, Olena, 2016. "Household electricity demand profiles – A high-resolution load model to facilitate modelling of energy flexible buildings," Energy, Elsevier, vol. 103(C), pages 487-501.
    5. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    6. Dennis J. Aigner & Cynts Sorooshian & Pamela Kerwin, 1984. "Conditional Demand Analysis for Estimating Residential End-Use Load Profiles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 81-98.
    7. Esther, B. Priya & Kumar, K. Sathish, 2016. "A survey on residential Demand Side Management architecture, approaches, optimization models and methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 342-351.
    8. Wright, Andrew & Firth, Steven, 2007. "The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations," Applied Energy, Elsevier, vol. 84(4), pages 389-403, April.
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