Multi-constraint building partitioning formulation for effective contaminant detection and isolation
| Main Authors: | Alexis Kyriacou, Stelios Timotheou, Michalis Michaelides, Christos Panayiotou, Marios Polycarpou |
|---|---|
| Format: | Proceeding Journal |
| Bahasa: | eng |
| Terbitan: |
, 2016
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| Online Access: |
https://zenodo.org/record/1252880 |
Daftar Isi:
- 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 an exact Mixed Integer Linear Programming (MILP) formulation for partitioning the building into smaller sections. The building is transformed into a graph which is partitioned into subgraphs indicating the groups of zones in each section while ensuring (i) maximum decoupling between the various subgraphs, (ii) strong connectivity between the zones of a subgraph and (iii) control of the number of allocated zones in each subgraph. 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 in real time, while ensuring the effective detection and isolation of contaminants in the building interior.
- A. Kyriacou, S. Timotheou, M. Michaelides, C. Panayiotou and M. Polycarpou, "Multi-constraint building partitioning formulation for effective contaminant detection and isolation," 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, pp. 4675-4682. doi: 10.1109/CEC.2016.7744387 © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works.