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Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

Author

Listed:
  • Amir Rafati

    (Department of Electrical Engineering, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman 6491796581, Iran)

  • Hamid Reza Shaker

    (Center for Energy Informatics, University of Southern Denmark, 5230 Odense, Denmark)

  • Saman Ghahghahzadeh

    (Department of Electrical Engineering, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman 6491796581, Iran)

Abstract

Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:341-:d:717648
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    References listed on IDEAS

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    1. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    2. Claudio Giovanni Mattera & Hamid Reza Shaker & Muhyiddine Jradi, 2019. "Consensus-Based Method for Anomaly Detection in VAV Units," Energies, MDPI, vol. 12(3), pages 1-17, February.
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    2. Luan, Wenpeng & Wei, Zun & Liu, Bo & Yu, Yixin, 2022. "Non-intrusive power waveform modeling and identification of air conditioning load," Applied Energy, Elsevier, vol. 324(C).
    3. Iivo Metsä-Eerola & Jukka Pulkkinen & Olli Niemitalo & Olli Koskela, 2022. "On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks," Energies, MDPI, vol. 15(14), pages 1-20, July.
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    5. Antonio Rosato & Marco Savino Piscitelli & Alfonso Capozzoli, 2023. "Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings," Energies, MDPI, vol. 16(2), pages 1-6, January.
    6. Khaled Almazam & Omar Humaidan & Nahla M. Shannan & Faizah Mohammed Bashir & Taha Gammoudi & Yakubu Aminu Dodo, 2025. "Innovative Energy Efficiency in HVAC Systems with an Integrated Machine Learning and Model Predictive Control Technique: A Prospective Toward Sustainable Buildings," Sustainability, MDPI, vol. 17(7), pages 1-35, March.
    7. Brudermueller, Tobias & Kreft, Markus & Fleisch, Elgar & Staake, Thorsten, 2023. "Large-scale monitoring of residential heat pump cycling using smart meter data," Applied Energy, Elsevier, vol. 350(C).
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    9. Liu, Yinyan & Ma, Jin & Xing, Xinjie & Liu, Xinglu & Wang, Wei, 2022. "A home energy management system incorporating data-driven uncertainty-aware user preference," Applied Energy, Elsevier, vol. 326(C).

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