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High resolution synthetic residential energy use profiles for the United States
Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, ele...
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/PMC9902520/ https://www.ncbi.nlm.nih.gov/pubmed/36746951 http://dx.doi.org/10.1038/s41597-022-01914-1 |
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author | Thorve, Swapna Baek, Young Yun Swarup, Samarth Mortveit, Henning Marathe, Achla Vullikanti, Anil Marathe, Madhav |
author_facet | Thorve, Swapna Baek, Young Yun Swarup, Samarth Mortveit, Henning Marathe, Achla Vullikanti, Anil Marathe, Madhav |
author_sort | Thorve, Swapna |
collection | PubMed |
description | Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States. |
format | Online Article Text |
id | pubmed-9902520 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99025202023-02-08 High resolution synthetic residential energy use profiles for the United States Thorve, Swapna Baek, Young Yun Swarup, Samarth Mortveit, Henning Marathe, Achla Vullikanti, Anil Marathe, Madhav Sci Data Data Descriptor Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States. Nature Publishing Group UK 2023-02-06 /pmc/articles/PMC9902520/ /pubmed/36746951 http://dx.doi.org/10.1038/s41597-022-01914-1 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 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/) . |
spellingShingle | Data Descriptor Thorve, Swapna Baek, Young Yun Swarup, Samarth Mortveit, Henning Marathe, Achla Vullikanti, Anil Marathe, Madhav High resolution synthetic residential energy use profiles for the United States |
title | High resolution synthetic residential energy use profiles for the United States |
title_full | High resolution synthetic residential energy use profiles for the United States |
title_fullStr | High resolution synthetic residential energy use profiles for the United States |
title_full_unstemmed | High resolution synthetic residential energy use profiles for the United States |
title_short | High resolution synthetic residential energy use profiles for the United States |
title_sort | high resolution synthetic residential energy use profiles for the united states |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902520/ https://www.ncbi.nlm.nih.gov/pubmed/36746951 http://dx.doi.org/10.1038/s41597-022-01914-1 |
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