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

Mehrnaz Anvari (), Elisavet Proedrou, Benjamin Schäfer, Christian Beck, Holger Kantz and Marc Timme ()
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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
Christian Beck: Queen Mary University of London
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

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31942-9

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DOI: 10.1038/s41467-022-31942-9

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