Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

Nurkamilya Daurenbayeva (), Almas Nurlanuly, Lyazzat Atymtayeva and Mateus Mendes ()
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Nurkamilya Daurenbayeva: Department of Computer Engineering, International Information Technology University, Almaty A15H7X9, Kazakhstan
Almas Nurlanuly: Department of Aviation Equipment and Technology, Academy of Civil Aviation, Almaty A35X2Y6, Kazakhstan
Lyazzat Atymtayeva: Department of Information Sciences, Suleyman Demirel University, Kaskelen 043801, Kazakhstan
Mateus Mendes: Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal

Energies, 2023, vol. 16, issue 8, 1-21

Abstract: 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: microclimate control systems; fault detection and diagnosis; prediction methods; machine learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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