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

Anvari, Mehrnaz ; Proedrou, Elisavet; Schäfer, Benjamin ORCID iD icon 1; Beck, Christian; Kantz, Holger; Timme, Marc
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000150016
Veröffentlicht am 17.08.2022
Originalveröffentlichung
DOI: 10.1038/s41467-022-31942-9
Scopus
Zitationen: 24
Dimensions
Zitationen: 28
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2041-1723
KITopen-ID: 1000150016
HGF-Programm 37.12.02 (POF IV, LK 01) Design,Operation & Digitalization of the Future Energy Grids
Erschienen in Nature Communications
Verlag Nature Research
Band 13
Heft 1
Seiten Art.-Nr.: 4593
Vorab online veröffentlicht am 06.08.2022
Nachgewiesen in Web of Science
Dimensions
Scopus
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