Data-Driven Modelling of Smart Building Ventilation Subsystem

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,...

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Bibliographic Details
Published in:Journal of sensors 2019-01, Vol.2019 (2019), p.1-14
Main Authors: Fagarasan, Ioana, Arghira, Nicoleta, Stamatescu, Iulia, Stamatescu, Grigore
Format: Article
Language:English
Subjects:
Algorithms
Architectural engineering
Building automation
Building management systems
Buildings
Commercial buildings
Control algorithms
Cost control
Data mining
Data processing
Data transmission
Electronic devices
Energy consumption
Energy efficiency
Fault detection
HVAC
International conferences
Internet of Things
Knowledge discovery
Mine ventilation
Modelling
Sensors
Subsystems
Support vector machines
System identification
Time series
Ventilation
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Summary: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:1687-725X
1687-7268
DOI:10.1155/2019/3572019