ArticleNature communications2022
Data-driven load profiles and the dynamics of residential electricity consumption.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Oil-impregnated densified wood veneer with high electrical insulation enabled by nanosized oil channels.Science advances · 2026Article
- Reducing the Carbon Footprint of Neovascular Age-Related Macular Degeneration Therapy: Environmental Impact of Transitioning to Aflibercept 8 mg.Journal of ophthalmology · 2026Article
- Multi-resolution dataset of electricity consumption in Chinese cities.Scientific data · 2025Article
- Data-driven dynamic modeling for inverter-based resources using neural networks.Nature communications · 2025Article
- Nonstandard power grid frequency statistics across continents.Scientific reports · 2025Article
- Globally interconnected solar-wind system addresses future electricity demands.Nature communications · 2025Article
- Actionable Forecasting as a Determinant of Biological Adaptation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Large-Scale Residential Load Dataset in a Southern Province of China.Scientific data · 2025Article
- Towards data-driven electricity management: multi-region uniform data and knowledge graph.Scientific data · 2025Article
- Article
- CRAWLING: a crowdsourcing algorithm on wheels for smart parking.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.