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A voltage and current measurement dataset for plug load appliance identification in households
This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered applianc...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015894/ https://www.ncbi.nlm.nih.gov/pubmed/32051418 http://dx.doi.org/10.1038/s41597-020-0389-7 |
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author | Medico, Roberto De Baets, Leen Gao, Jingkun Giri, Suman Kara, Emre Dhaene, Tom Develder, Chris Bergés, Mario Deschrijver, Dirk |
author_facet | Medico, Roberto De Baets, Leen Gao, Jingkun Giri, Suman Kara, Emre Dhaene, Tom Develder, Chris Bergés, Mario Deschrijver, Dirk |
author_sort | Medico, Roberto |
collection | PubMed |
description | This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered appliances from 17 different appliance types (e.g., refrigerators, microwave ovens, etc.) comprising 330 different makes and models, and collected at 65 different locations in Pittsburgh, Pennsylvania (USA). Additionally, PLAID contains 1314 records of the combined operation of 13 of these appliance types (i.e., measurements obtained when multiple appliances were active simultaneously). Identifying electrical appliances based on electrical measurements is of importance in demand-side management applications for the electrical power grid including automated load control, load scheduling and non-intrusive load monitoring. This paper provides a systematic description of the measurement setup and dataset so that it can be used to develop and benchmark new methods in these and other applications, and so that extensions to it can be developed and incorporated in a consistent manner. |
format | Online Article Text |
id | pubmed-7015894 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70158942020-03-03 A voltage and current measurement dataset for plug load appliance identification in households Medico, Roberto De Baets, Leen Gao, Jingkun Giri, Suman Kara, Emre Dhaene, Tom Develder, Chris Bergés, Mario Deschrijver, Dirk Sci Data Data Descriptor This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered appliances from 17 different appliance types (e.g., refrigerators, microwave ovens, etc.) comprising 330 different makes and models, and collected at 65 different locations in Pittsburgh, Pennsylvania (USA). Additionally, PLAID contains 1314 records of the combined operation of 13 of these appliance types (i.e., measurements obtained when multiple appliances were active simultaneously). Identifying electrical appliances based on electrical measurements is of importance in demand-side management applications for the electrical power grid including automated load control, load scheduling and non-intrusive load monitoring. This paper provides a systematic description of the measurement setup and dataset so that it can be used to develop and benchmark new methods in these and other applications, and so that extensions to it can be developed and incorporated in a consistent manner. Nature Publishing Group UK 2020-02-12 /pmc/articles/PMC7015894/ /pubmed/32051418 http://dx.doi.org/10.1038/s41597-020-0389-7 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Medico, Roberto De Baets, Leen Gao, Jingkun Giri, Suman Kara, Emre Dhaene, Tom Develder, Chris Bergés, Mario Deschrijver, Dirk A voltage and current measurement dataset for plug load appliance identification in households |
title | A voltage and current measurement dataset for plug load appliance identification in households |
title_full | A voltage and current measurement dataset for plug load appliance identification in households |
title_fullStr | A voltage and current measurement dataset for plug load appliance identification in households |
title_full_unstemmed | A voltage and current measurement dataset for plug load appliance identification in households |
title_short | A voltage and current measurement dataset for plug load appliance identification in households |
title_sort | voltage and current measurement dataset for plug load appliance identification in households |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015894/ https://www.ncbi.nlm.nih.gov/pubmed/32051418 http://dx.doi.org/10.1038/s41597-020-0389-7 |
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