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Title: A performance evaluation framework for building fault detection and diagnosis algorithms

Abstract

Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products - a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.

Authors:
 [1];  [2];  [1];  [3];  [4];  [2]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. TRC, Oakland, CA (United States)
  4. Univ. of Michigan, Ann Arbor, MI (United States)
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:
1505931
Report Number(s):
NREL/JA-5500-72800
Journal ID: ISSN 0378-7788
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 192; Journal Issue: C; Journal ID: ISSN 0378-7788
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; fault detection and diagnosis; performance evaluation; algorithm testing; benchmarking; building systems; building energy performance

Citation Formats

Frank, Stephen, Lin, Guanjing, Jin, Xin, Singla, Rupam, Farthing, Amanda, and Granderson, Jessica. A performance evaluation framework for building fault detection and diagnosis algorithms. United States: N. p., 2019. Web. doi:10.1016/j.enbuild.2019.03.024.
Frank, Stephen, Lin, Guanjing, Jin, Xin, Singla, Rupam, Farthing, Amanda, & Granderson, Jessica. A performance evaluation framework for building fault detection and diagnosis algorithms. United States. doi:10.1016/j.enbuild.2019.03.024.
Frank, Stephen, Lin, Guanjing, Jin, Xin, Singla, Rupam, Farthing, Amanda, and Granderson, Jessica. Tue . "A performance evaluation framework for building fault detection and diagnosis algorithms". United States. doi:10.1016/j.enbuild.2019.03.024.
@article{osti_1505931,
title = {A performance evaluation framework for building fault detection and diagnosis algorithms},
author = {Frank, Stephen and Lin, Guanjing and Jin, Xin and Singla, Rupam and Farthing, Amanda and Granderson, Jessica},
abstractNote = {Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products - a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.},
doi = {10.1016/j.enbuild.2019.03.024},
journal = {Energy and Buildings},
number = C,
volume = 192,
place = {United States},
year = {2019},
month = {3}
}

Journal Article:
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This content will become publicly available on March 12, 2020
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