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Title: 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:
 [1];  [1];  [1];  [2]; ORCiD logo [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Oak Ridge National Lab. (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. doi: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. doi: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 = {2020},
month = {2}
}

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