Building fault detection data to aid diagnostic algorithm creation and performance testing
Abstract
It is estimated that approximately 4–5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- OSTI Identifier:
- 1603785
- Alternate Identifier(s):
- OSTI ID: 1615305; OSTI ID: 1761681
- Report Number(s):
- PNNL-SA-150821
Journal ID: ISSN 2052-4463
- Grant/Contract Number:
- AC05-76RL01830; AC02-05CH11231; AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Data
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2052-4463
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
Citation Formats
Granderson, J, Lin, Guanjing, Harding, Ari, Im, Piljae, and Chen, Yan. Building fault detection data to aid diagnostic algorithm creation and performance testing. United States: N. p., 2020.
Web. doi:10.1038/s41597-020-0398-6.
Granderson, J, Lin, Guanjing, Harding, Ari, Im, Piljae, & Chen, Yan. Building fault detection data to aid diagnostic algorithm creation and performance testing. United States. https://doi.org/10.1038/s41597-020-0398-6
Granderson, J, Lin, Guanjing, Harding, Ari, Im, Piljae, and Chen, Yan. Mon .
"Building fault detection data to aid diagnostic algorithm creation and performance testing". United States. https://doi.org/10.1038/s41597-020-0398-6. https://www.osti.gov/servlets/purl/1603785.
@article{osti_1603785,
title = {Building fault detection data to aid diagnostic algorithm creation and performance testing},
author = {Granderson, J and Lin, Guanjing and Harding, Ari and Im, Piljae and Chen, Yan},
abstractNote = {It is estimated that approximately 4–5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.},
doi = {10.1038/s41597-020-0398-6},
journal = {Scientific Data},
number = 1,
volume = 7,
place = {United States},
year = {Mon Feb 24 00:00:00 EST 2020},
month = {Mon Feb 24 00:00:00 EST 2020}
}
Web of Science
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