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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Mehrnaz Anvari - , Potsdam Institute for Climate Impact Research (Autor:in)
  • Elisavet Proedrou - , German Aerospace Center (Autor:in)
  • Benjamin Schäfer - , Queen Mary University of London, Norwegian University of Life Sciences, Karlsruhe Institute of Technology (Autor:in)
  • Christian Beck - , Queen Mary University of London, Alan Turing Institute (Autor:in)
  • Holger Kantz - , Max-Planck-Institute for the Physics of Complex Systems (Autor:in)
  • Marc Timme - , Professur für Netzwerk-Dynamik (Fakultät Physik), Professur für Netzwerk-Dynamik (cfaed), Lakeside Labs GmbH (Autor:in)

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.

Details

OriginalspracheEnglisch
Aufsatznummer4593
FachzeitschriftNature communications
Jahrgang13
Ausgabenummer1
PublikationsstatusVeröffentlicht - 6 Aug. 2022
Peer-Review-StatusJa

Externe IDs

PubMed 35933555