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

Anvari, M., Proedrou, E., Schaefer, B., Beck, C., Kantz, H., & Timme, M. (2022). Data-driven load profiles and the dynamics of residential electricity consumption. Nature Communications, 13(1): 4593. doi:10.1038/s41467-022-31942-9.

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2009.09287.pdf (Preprint), 4MB
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Anvari, Mehrnaz1, Author
Proedrou, Elisavet1, Author
Schaefer, Benjamin1, Author
Beck, Christian1, Author
Kantz, Holger2, Author           
Timme, Marc1, Author
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1external, ou_persistent22              
2Max Planck Institute for the Physics of Complex Systems, Max Planck Society, ou_2117288              

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 Abstract: 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.
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.

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Language(s): eng - English
 Dates: 2022-08-062022-08-06
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: ISI: 000836839500020
DOI: 10.1038/s41467-022-31942-9
arXiv: 2009.09287
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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 13 (1) Sequence Number: 4593 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723