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Author Stamatescu, Grigore ♦ Stamatescu, Iulia ♦ Arghira, Nicoleta ♦ Fagarasan, Ioana
Editor Potirakis, Stelios M.
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2019
Language English
Abstract Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.
ISSN 1687725X
Learning Resource Type Article
Publisher Date 2019-03-12
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 16877268
Journal Journal of Sensors
Volume Number 2019
Page Count 14