| Description |
1 online resource (14 pages) : color illustrations. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
NREL/CP ; 5500-65924 |
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Conference paper (National Renewable Energy Laboratory (U.S.)) ; NREL/CP-5000-65926.
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| Note |
Online resource; title from PDF title page (Energy, viewed September 2, 2016). |
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Published through SciTech Connect. |
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"August 2016." |
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"To be presented at the 2016 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, California, 21-26 August 2016." |
| Summary |
Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy. |
| Bibliography |
Includes bibliographical references (pages 12-14). |
| Funding |
Sponsored by National Renewable Energy Laboratory AC36-08GO28308 |
| Subject |
Commercial buildings -- Energy conservation -- United States.
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Energy Conservation, Consumption, And Utilization.
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Commercial buildings -- Energy conservation.
(OCoLC)fst00869390
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United States. (OCoLC)fst01204155
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| Genre/Form |
Online resources.
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Electronic books.
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Electronic government information.
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| Added Author |
National Renewable Energy Laboratory (U.S.), issuing body.
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| Gpo Item No. |
0430-P-04 (online) |
| Sudoc No. |
E 9.17:NREL/CP-5000-65924 |
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