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Title: Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

Abstract

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.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1290794
Report Number(s):
NREL/CP-5500-65924
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the 2016 ACEEE Summer Study on Energy Efficiency in Buildings, 21-26 August 2016, Pacific Grove, California
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; commercial buildings; fault detection and diagnosis; FDD; building energy models

Citation Formats

Frank, Stephen, Heaney, Michael, Jin, Xin, Robertson, Joseph, Cheung, Howard, Elmore, Ryan, and Henze, Gregor. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint. United States: N. p., 2016. Web.
Frank, Stephen, Heaney, Michael, Jin, Xin, Robertson, Joseph, Cheung, Howard, Elmore, Ryan, & Henze, Gregor. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint. United States.
Frank, Stephen, Heaney, Michael, Jin, Xin, Robertson, Joseph, Cheung, Howard, Elmore, Ryan, and Henze, Gregor. Mon . "Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1290794.
@article{osti_1290794,
title = {Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint},
author = {Frank, Stephen and Heaney, Michael and Jin, Xin and Robertson, Joseph and Cheung, Howard and Elmore, Ryan and Henze, Gregor},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Aug 01 00:00:00 EDT 2016},
month = {Mon Aug 01 00:00:00 EDT 2016}
}

Conference:
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