Consensus-Based Method for Anomaly Detection in VAV Units

Claudio Giovanni Mattera, Hamid Reza Shaker and Muhyiddine Jradi
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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

Energies, 2019, vol. 12, issue 3, 1-17

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.

Keywords: fault detection and diagnosis; consensus; smart buildings (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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