ArticleScientific data2023
ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- High-resolution energy data from a sustainable industrial production area in Karlsruhe.Scientific data · 2026Article
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- ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors.Scientific data · 2023Article
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Authors and funding
4 authors.
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Abstract
The combination of ongoing urban expansion and electrification of uses challenges the power grid. In such a context, information regarding customers' consumption is vital to assess the expected load at strategic nodes over time, and to guide power system planning strategies. Comprehensive household consumption databases are widely available today thanks to the roll-out of smart meters, while the consumption of tertiary premises is seldom shared mainly due to privacy concerns. To fill this gap, the French main distribution system operator, Enedis, commissioned Mines Paris to derive load profiles of industrial and tertiary sectors for its prospective tools. The ELMAS dataset is an open dataset of 18 electricity load profiles derived from hourly consumption time series collected continuously over one year from a total of 55,730 customers. These customers are divided into 424 fields of activity, and three levels of capacity subscription. A clustering approach is employed to gather activities sharing similar temporal patterns, before averaging the associated time series to ensure anonymity.
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