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Consensus-Based Method for Anomaly Detection in VAV Units

Author

Listed:
  • Claudio Giovanni Mattera

    (Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), 5230 Odense, Denmark)

  • Hamid Reza Shaker

    (Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), 5230 Odense, Denmark)

  • Muhyiddine Jradi

    (Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), 5230 Odense, Denmark)

Abstract

Buildings account for large part of global energy consumption. Besides energy consumed due to normal operation, a large amount of energy can be wasted due to faults in buildings subsystems. Fault detection and diagnostics techniques aim to identify faults and prevent energy waste, but are often difficult to apply in practice. Data-driven methods, in particular, require an adequate amount of fault-free training data, which is rarely available. In this paper, we propose a method for anomaly detection that exploits consensus among multiple identical components. Even if some of the components are faulty, their aggregate behaviour is overall correct, and it can be used to train a data-driven model. We test our method on variable-air-volume units in an existing building, executing two experiments grouping the components according to ventilation unit, and according to room type. The two experiments identified the same set of anomalous components, i.e., their behaviour was different from the rest of the group in both cases, and this suggests that the anomaly was not due to wrong group assignment. The proposed method shows the potential of exploiting consensus among multiple identical systems to detect anomalous ones.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:468-:d:202642
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    References listed on IDEAS

    as
    1. Sang-Ji Lee & Jin-Young Choi & Hyung-Joo Lee & Dong-Jun Won, 2017. "Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm," Energies, MDPI, vol. 10(7), pages 1-16, July.
    2. Nian Liu & Bin Guo & Zifa Liu & Yongli Wang, 2018. "Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach," Energies, MDPI, vol. 11(7), pages 1-18, July.
    3. Claudio Giovanni Mattera & Muhyiddine Jradi & Hamid Reza Shaker, 2018. "Online Energy Simulator for building fault detection and diagnostics using dynamic energy performance model," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(3), pages 231-239.
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    Cited by:

    1. Hamidreza Alavi & Nuria Forcada, 2022. "User-Centric BIM-Based Framework for HVAC Root-Cause Detection," Energies, MDPI, vol. 15(10), pages 1-13, May.
    2. 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.

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