LAPSE:2023.34945
Published Article
LAPSE:2023.34945
Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems
April 28, 2023
An appropriate microclimate is one of the most important factors of a healthy and comfortable life. The microclimate of a place is determined by the temperature, humidity and speed of the air. Those factors determine how a person feels thermal comfort and, therefore, they play an essential role in people’s lives. Control of microclimate parameters is a very important topic for buildings, as well as greenhouses, where adequate microclimate is fundamental for best-growing results. Microclimate systems require adequate monitoring and maintenance, for their failure or suboptimal performance can increase energy consumption and have catastrophic results. In recent years, Fault Detection and Diagnosis in microclimate systems have been paid more attention. The main goal of those systems is to effectively detect faults and accurately isolate them to a failing component in the shortest time possible. Sometimes it is even possible to predict and anticipate failures, which allows preventing the failures from happening if appropriate measures are taken in time. The present paper reviews the state of the art in fault detection and diagnosis methods. It shows the growing importance of the topic and highlights important open research questions.
Keywords
fault detection and diagnosis, Machine Learning, microclimate control systems, prediction methods
Suggested Citation
Daurenbayeva N, Nurlanuly A, Atymtayeva L, Mendes M. Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems. (2023). LAPSE:2023.34945
Author Affiliations
Daurenbayeva N: Department of Computer Engineering, International Information Technology University, Almaty A15H7X9, Kazakhstan [ORCID]
Nurlanuly A: Department of Aviation Equipment and Technology, Academy of Civil Aviation, Almaty A35X2Y6, Kazakhstan [ORCID]
Atymtayeva L: Department of Information Sciences, Suleyman Demirel University, Kaskelen 043801, Kazakhstan [ORCID]
Mendes M: Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal; Institute of Systems and Robotics, University of Coimbra, Rua Silvio Lima-Polo II, 3030-290 Coimbra, Portugal [ORCID]
Journal Name
Energies
Volume
16
Issue
8
First Page
3508
Year
2023
Publication Date
2023-04-18
Published Version
ISSN
1996-1073
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Original Submission
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PII: en16083508, Publication Type: Review
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LAPSE:2023.34945
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doi:10.3390/en16083508
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Apr 28, 2023
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