Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review

William Nelson and Charles Culp
Additional contact information
William Nelson: Department of Mechanical Engineering, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA
Charles Culp: Department of Architecture, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA

Energies, 2022, vol. 15, issue 15, 1-20

Abstract: 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.

Keywords: fault detection; fault diagnosis; machine learning; building systems; HVAC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/15/5534/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/15/5534/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:15:p:5534-:d:876157

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().