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Title: Automatic building partitioning for effective contaminant detection and isolation
Authors: Kyriacou, Alexis 
Michaelides, Michalis P. 
Panayiotou, Christos G. 
metadata.dc.contributor.other: Μιχαηλίδης, Μιχάλης Π.
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Air quality;Clustering algorithms;Buildings
Issue Date: 20-Jun-2016
Source: 18th Mediterranean Electrotechnical Conference, 2016, Limassol, Cyprus
Conference: Mediterranean Electrotechnical Conference 
Abstract: Intelligent buildings are responsible for ensuring the indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events due to accidental or malicious actions. An emerging environmental control application is monitoring the intelligent buildings against the presence of such events, by incorporating various sensing technologies and distributed detection and isolation algorithms. The needed simplicity, the improved scalability and fault tolerance are some of the main reasons for choosing distributed approaches over centralized ones. Hence, the effective partitioning of buildings into smaller sections for contaminant detection and isolation approaches is of great importance. In this paper, we present a heuristic algorithm for partitioning the building into smaller sections. The proposed algorithm is based on matrix clustering techniques and groups the building zones in order to form the different sections while ensuring (i) maximum decoupling between the various sections, (ii) strong connectivity between the zones of a section and (iii) fairness with respect to the number of allocated zones. The partitioning is achieved in near real time, providing the ability of repartitioning and adapting to the dynamic nature of the airflows. The main contribution of this work is the automatic partitioning of the building into sections, which enables the distributed simulation, modeling, analysis and management of the intelligent building while ensuring the effective detection and isolation of contaminants in the building interior.
ISBN: 978-150900057-9
ISSN: 2158-8481
DOI: 10.1109/MELCON.2016.7495469
Rights: © 2016 IEEE.
Type: Conference Papers
Affiliation : University of Cyprus 
Cyprus University of Technology 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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