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ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562465/ https://www.ncbi.nlm.nih.gov/pubmed/37813916 http://dx.doi.org/10.1038/s41597-023-02542-z |
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author | Bellinguer, Kevin Girard, Robin Bocquet, Alexis Chevalier, Antoine |
author_facet | Bellinguer, Kevin Girard, Robin Bocquet, Alexis Chevalier, Antoine |
author_sort | Bellinguer, Kevin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10562465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105624652023-10-11 ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors Bellinguer, Kevin Girard, Robin Bocquet, Alexis Chevalier, Antoine Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-10-09 /pmc/articles/PMC10562465/ /pubmed/37813916 http://dx.doi.org/10.1038/s41597-023-02542-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Bellinguer, Kevin Girard, Robin Bocquet, Alexis Chevalier, Antoine ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors |
title | ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors |
title_full | ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors |
title_fullStr | ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors |
title_full_unstemmed | ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors |
title_short | ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors |
title_sort | elmas: a one-year dataset of hourly electrical load profiles from 424 french industrial and tertiary sectors |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562465/ https://www.ncbi.nlm.nih.gov/pubmed/37813916 http://dx.doi.org/10.1038/s41597-023-02542-z |
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