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

Mehrnaz Anvari; Elisavet Proedrou; Benjamin Schäfer; Christian Beck; Holger Kantz; Marc Timme

doi:10.1038/s41467-022-31942-9

Nature Communications (Aug 2022)

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

Mehrnaz Anvari,
Elisavet Proedrou,
Benjamin Schäfer,
Christian Beck,
Holger Kantz,
Marc Timme

Affiliations

Mehrnaz Anvari: Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association
Elisavet Proedrou: DLR Institute for Networked Energy Systems
Benjamin Schäfer: School of Mathematical Sciences, Queen Mary University of London
Christian Beck: School of Mathematical Sciences, 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

DOI: https://doi.org/10.1038/s41467-022-31942-9
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

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In modern power grids, knowing the required electric power demand and its variations is necessary to balance demand and supply. The authors propose a data-driven approach to create high-resolution load profiles and characterize their fluctuations, based on recorded data of electricity consumption.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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