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An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study
Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5315495/ https://www.ncbi.nlm.nih.gov/pubmed/28055033 http://dx.doi.org/10.1038/sdata.2016.122 |
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author | Murray, David Stankovic, Lina Stankovic, Vladimir |
author_facet | Murray, David Stankovic, Lina Stankovic, Vladimir |
author_sort | Murray, David |
collection | PubMed |
description | Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data. |
format | Online Article Text |
id | pubmed-5315495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53154952017-03-01 An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study Murray, David Stankovic, Lina Stankovic, Vladimir Sci Data Data Descriptor Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data. Nature Publishing Group 2017-01-05 /pmc/articles/PMC5315495/ /pubmed/28055033 http://dx.doi.org/10.1038/sdata.2016.122 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. |
spellingShingle | Data Descriptor Murray, David Stankovic, Lina Stankovic, Vladimir An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study |
title | An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study |
title_full | An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study |
title_fullStr | An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study |
title_full_unstemmed | An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study |
title_short | An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study |
title_sort | electrical load measurements dataset of united kingdom households from a two-year longitudinal study |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5315495/ https://www.ncbi.nlm.nih.gov/pubmed/28055033 http://dx.doi.org/10.1038/sdata.2016.122 |
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