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

Author

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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
    Institute of Systems and Robotics, University of Coimbra, Rua Silvio Lima-Polo II, 3030-290 Coimbra, Portugal)

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.

Suggested Citation

  • Nurkamilya Daurenbayeva & Almas Nurlanuly & Lyazzat Atymtayeva & Mateus Mendes, 2023. "Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems," Energies, MDPI, vol. 16(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3508-:d:1126273
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    References listed on IDEAS

    as
    1. Balduíno César Mateus & Mateus Mendes & José Torres Farinha & Rui Assis & António Marques Cardoso, 2021. "Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press," Energies, MDPI, vol. 14(21), pages 1-21, October.
    2. João Antunes Rodrigues & José Torres Farinha & Mateus Mendes & Ricardo J. G. Mateus & António J. Marques Cardoso, 2022. "Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition," Energies, MDPI, vol. 15(17), pages 1-16, August.
    3. Amir Rafati & Hamid Reza Shaker & Saman Ghahghahzadeh, 2022. "Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review," Energies, MDPI, vol. 15(1), pages 1-16, January.
    4. Kristian Fabbri & Jacopo Gaspari & Laura Vandi, 2019. "Indoor Thermal Comfort of Pregnant Women in Hospital: A Case Study Evidence," Sustainability, MDPI, vol. 11(23), pages 1-24, November.
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    1. Balduíno César Mateus & José Torres Farinha & Mateus Mendes, 2024. "Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks," Energies, MDPI, vol. 17(2), pages 1-18, January.

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