Next Article in Journal
Kinetics and Reactor Design Aspects of Selective Methanation of CO over a Ru/γ-Al2O3 Catalyst in CO2/H2 Rich Gases
Previous Article in Journal
Rheological Characteristics of Molten Salt Seeded with Al2O3 Nanopowder and Graphene for Concentrated Solar Power
Open AccessArticle

Consensus-Based Method for Anomaly Detection in VAV Units

Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), 5230 Odense, Denmark
Author to whom correspondence should be addressed.
Energies 2019, 12(3), 468;
Received: 3 December 2018 / Revised: 28 December 2018 / Accepted: 30 January 2019 / Published: 1 February 2019
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. View Full-Text
Keywords: fault detection and diagnosis; consensus; smart buildings fault detection and diagnosis; consensus; smart buildings
Show Figures

Graphical abstract

MDPI and ACS Style

Mattera, C.G.; Shaker, H.R.; Jradi, M. Consensus-Based Method for Anomaly Detection in VAV Units. Energies 2019, 12, 468.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop