Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance
Abstract
Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97 M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. Finally, it showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that canmore »
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- OSTI Identifier:
- 1603598
- Alternate Identifier(s):
- OSTI ID: 1607548
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Building and Environment
- Additional Journal Information:
- Journal Volume: 168; Journal Issue: C; Journal ID: ISSN 0360-1323
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; fault detection and diagnostics; energy efficiency; savings and cost; performance evaluation; algorithm testing; data
Citation Formats
Lin, Guanjing, Kramer, Hannah, and Granderson, Jessica. Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance. United States: N. p., 2019.
Web. doi:10.1016/j.buildenv.2019.106505.
Lin, Guanjing, Kramer, Hannah, & Granderson, Jessica. Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance. United States. https://doi.org/10.1016/j.buildenv.2019.106505
Lin, Guanjing, Kramer, Hannah, and Granderson, Jessica. Mon .
"Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance". United States. https://doi.org/10.1016/j.buildenv.2019.106505. https://www.osti.gov/servlets/purl/1603598.
@article{osti_1603598,
title = {Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance},
author = {Lin, Guanjing and Kramer, Hannah and Granderson, Jessica},
abstractNote = {Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97 M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. Finally, it showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset.},
doi = {10.1016/j.buildenv.2019.106505},
journal = {Building and Environment},
number = C,
volume = 168,
place = {United States},
year = {Mon Nov 04 00:00:00 EST 2019},
month = {Mon Nov 04 00:00:00 EST 2019}
}
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Works referencing / citing this record:
Building fault detection data to aid diagnostic algorithm creation and performance testing
journal, February 2020
- Granderson, Jessica; Lin, Guanjing; Harding, Ari
- Scientific Data, Vol. 7, Issue 1
Efficient and robust optimization for building energy simulation
journal, June 2016
- Pourarian, Shokouh; Kearsley, Anthony; Wen, Jin
- Energy and Buildings, Vol. 122
Building fault detection data to aid diagnostic algorithm creation and performance testing
journal, February 2020
- Granderson, Jessica; Lin, Guanjing; Harding, Ari
- Scientific Data, Vol. 7, Issue 1