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Time series of useful energy consumption patterns for energy system modeling
The analysis of energy scenarios for future energy systems requires appropriate data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce. In our JERICHO-E-usage dataset, we provide comprehensive data on useful ene...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166825/ https://www.ncbi.nlm.nih.gov/pubmed/34059689 http://dx.doi.org/10.1038/s41597-021-00907-w |
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author | Priesmann, Jan Nolting, Lars Kockel, Christina Praktiknjo, Aaron |
author_facet | Priesmann, Jan Nolting, Lars Kockel, Christina Praktiknjo, Aaron |
author_sort | Priesmann, Jan |
collection | PubMed |
description | The analysis of energy scenarios for future energy systems requires appropriate data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce. In our JERICHO-E-usage dataset, we provide comprehensive data on useful energy consumption patterns for heat, cold, mechanical energy, information and communication, and light in high spatial and temporal resolution. Furthermore, we distinguish between residential, industrial, commerce, and mobility consumers. For our dataset, we aggregate bottom-up data and disaggregate top-down data both to the NUTS2 level. The NUTS2 level serves as an interface to validate our combined method approach and the calculations. We combine a multitude of data sources such as weather time series, standard load profiles, census data, movement data, and employment figures to increase the scope, validity, and reproducibility for energy system modeling. The focus of our JERICHO-E-usage dataset on useful energy consumption might be of particular interest to researchers who analyze energy scenarios where renewable electricity is largely substituted for fossil fuel (sector coupling). |
format | Online Article Text |
id | pubmed-8166825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81668252021-06-15 Time series of useful energy consumption patterns for energy system modeling Priesmann, Jan Nolting, Lars Kockel, Christina Praktiknjo, Aaron Sci Data Data Descriptor The analysis of energy scenarios for future energy systems requires appropriate data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce. In our JERICHO-E-usage dataset, we provide comprehensive data on useful energy consumption patterns for heat, cold, mechanical energy, information and communication, and light in high spatial and temporal resolution. Furthermore, we distinguish between residential, industrial, commerce, and mobility consumers. For our dataset, we aggregate bottom-up data and disaggregate top-down data both to the NUTS2 level. The NUTS2 level serves as an interface to validate our combined method approach and the calculations. We combine a multitude of data sources such as weather time series, standard load profiles, census data, movement data, and employment figures to increase the scope, validity, and reproducibility for energy system modeling. The focus of our JERICHO-E-usage dataset on useful energy consumption might be of particular interest to researchers who analyze energy scenarios where renewable electricity is largely substituted for fossil fuel (sector coupling). Nature Publishing Group UK 2021-05-31 /pmc/articles/PMC8166825/ /pubmed/34059689 http://dx.doi.org/10.1038/s41597-021-00907-w Text en © The Author(s) 2021 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Priesmann, Jan Nolting, Lars Kockel, Christina Praktiknjo, Aaron Time series of useful energy consumption patterns for energy system modeling |
title | Time series of useful energy consumption patterns for energy system modeling |
title_full | Time series of useful energy consumption patterns for energy system modeling |
title_fullStr | Time series of useful energy consumption patterns for energy system modeling |
title_full_unstemmed | Time series of useful energy consumption patterns for energy system modeling |
title_short | Time series of useful energy consumption patterns for energy system modeling |
title_sort | time series of useful energy consumption patterns for energy system modeling |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166825/ https://www.ncbi.nlm.nih.gov/pubmed/34059689 http://dx.doi.org/10.1038/s41597-021-00907-w |
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