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A labeled dataset for building HVAC systems operating in faulted and fault-free states
Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes t...
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/PMC10235024/ https://www.ncbi.nlm.nih.gov/pubmed/37264019 http://dx.doi.org/10.1038/s41597-023-02197-w |
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author | Granderson, Jessica Lin, Guanjing Chen, Yimin Casillas, Armando Wen, Jin Chen, Zhelun Im, Piljae Huang, Sen Ling, Jiazhen |
author_facet | Granderson, Jessica Lin, Guanjing Chen, Yimin Casillas, Armando Wen, Jin Chen, Zhelun Im, Piljae Huang, Sen Ling, Jiazhen |
author_sort | Granderson, Jessica |
collection | PubMed |
description | Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020. |
format | Online Article Text |
id | pubmed-10235024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102350242023-06-03 A labeled dataset for building HVAC systems operating in faulted and fault-free states Granderson, Jessica Lin, Guanjing Chen, Yimin Casillas, Armando Wen, Jin Chen, Zhelun Im, Piljae Huang, Sen Ling, Jiazhen Sci Data Data Descriptor Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020. Nature Publishing Group UK 2023-06-01 /pmc/articles/PMC10235024/ /pubmed/37264019 http://dx.doi.org/10.1038/s41597-023-02197-w 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 Granderson, Jessica Lin, Guanjing Chen, Yimin Casillas, Armando Wen, Jin Chen, Zhelun Im, Piljae Huang, Sen Ling, Jiazhen A labeled dataset for building HVAC systems operating in faulted and fault-free states |
title | A labeled dataset for building HVAC systems operating in faulted and fault-free states |
title_full | A labeled dataset for building HVAC systems operating in faulted and fault-free states |
title_fullStr | A labeled dataset for building HVAC systems operating in faulted and fault-free states |
title_full_unstemmed | A labeled dataset for building HVAC systems operating in faulted and fault-free states |
title_short | A labeled dataset for building HVAC systems operating in faulted and fault-free states |
title_sort | labeled dataset for building hvac systems operating in faulted and fault-free states |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235024/ https://www.ncbi.nlm.nih.gov/pubmed/37264019 http://dx.doi.org/10.1038/s41597-023-02197-w |
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