LAPSE:2023.10968
Published Article
LAPSE:2023.10968
Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review
February 27, 2023
Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Research towards automated fault detection and diagnostics has accelerated in recent history. Rule-based methods have been developed for decades to great success, but recent advances in computing power have opened new doors for more complex processing techniques which could be used for more accurate results. Popular machine learning algorithms may often be applied in both unsupervised and supervised contexts, for both classification and regression outputs. Significant research has been performed in all permutations of these divisions using algorithms such as support vector machines, neural networks, Bayesian networks, and a variety of clustering techniques. An evaluation of the remaining obstacles towards widespread adoption of these algorithms, in both commercial and scientific domains, is made. Resolutions for these obstacles are proposed and discussed.
Record ID
Keywords
building systems, Fault Detection, fault diagnosis, HVAC, Machine Learning
Subject
Suggested Citation
Nelson W, Culp C. Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review. (2023). LAPSE:2023.10968
Author Affiliations
Nelson W: Department of Mechanical Engineering, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA
Culp C: Department of Architecture, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA [ORCID]
Culp C: Department of Architecture, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA [ORCID]
Journal Name
Energies
Volume
15
Issue
15
First Page
5534
Year
2022
Publication Date
2022-07-30
Published Version
ISSN
1996-1073
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Original Submission
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PII: en15155534, Publication Type: Review
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LAPSE:2023.10968
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doi:10.3390/en15155534
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[v1] (Original Submission)
Feb 27, 2023
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Feb 27, 2023
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