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CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets
This paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m(2) office building located in Bangkok, Thailand. The electricity consumption data (kW) are that of individual air conditionin...
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/PMC7371880/ https://www.ncbi.nlm.nih.gov/pubmed/32686680 http://dx.doi.org/10.1038/s41597-020-00582-3 |
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author | Pipattanasomporn, Manisa Chitalia, Gopal Songsiri, Jitkomut Aswakul, Chaodit Pora, Wanchalerm Suwankawin, Surapong Audomvongseree, Kulyos Hoonchareon, Naebboon |
author_facet | Pipattanasomporn, Manisa Chitalia, Gopal Songsiri, Jitkomut Aswakul, Chaodit Pora, Wanchalerm Suwankawin, Surapong Audomvongseree, Kulyos Hoonchareon, Naebboon |
author_sort | Pipattanasomporn, Manisa |
collection | PubMed |
description | This paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m(2) office building located in Bangkok, Thailand. The electricity consumption data (kW) are that of individual air conditioning units, lighting, and plug loads in each of the 33 zones of the building. The indoor environmental sensor data comprise temperature (°C), relative humidity (%), and ambient light (lux) measurements of the same zones. The entire datasets are available at one-minute intervals for the period of 18 months from July 1, 2018, to December 31, 2019. Such datasets can be used to support a wide range of applications, such as zone-level, floor-level, and building-level load forecasting, indoor thermal model development, validation of building simulation models, development of demand response algorithms by load type, anomaly detection methods, and reinforcement learning algorithms for control of multiple AC units. |
format | Online Article Text |
id | pubmed-7371880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73718802020-07-22 CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets Pipattanasomporn, Manisa Chitalia, Gopal Songsiri, Jitkomut Aswakul, Chaodit Pora, Wanchalerm Suwankawin, Surapong Audomvongseree, Kulyos Hoonchareon, Naebboon Sci Data Data Descriptor This paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m(2) office building located in Bangkok, Thailand. The electricity consumption data (kW) are that of individual air conditioning units, lighting, and plug loads in each of the 33 zones of the building. The indoor environmental sensor data comprise temperature (°C), relative humidity (%), and ambient light (lux) measurements of the same zones. The entire datasets are available at one-minute intervals for the period of 18 months from July 1, 2018, to December 31, 2019. Such datasets can be used to support a wide range of applications, such as zone-level, floor-level, and building-level load forecasting, indoor thermal model development, validation of building simulation models, development of demand response algorithms by load type, anomaly detection methods, and reinforcement learning algorithms for control of multiple AC units. Nature Publishing Group UK 2020-07-20 /pmc/articles/PMC7371880/ /pubmed/32686680 http://dx.doi.org/10.1038/s41597-020-00582-3 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 Pipattanasomporn, Manisa Chitalia, Gopal Songsiri, Jitkomut Aswakul, Chaodit Pora, Wanchalerm Suwankawin, Surapong Audomvongseree, Kulyos Hoonchareon, Naebboon CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets |
title | CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets |
title_full | CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets |
title_fullStr | CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets |
title_full_unstemmed | CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets |
title_short | CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets |
title_sort | cu-bems, smart building electricity consumption and indoor environmental sensor datasets |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371880/ https://www.ncbi.nlm.nih.gov/pubmed/32686680 http://dx.doi.org/10.1038/s41597-020-00582-3 |
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