  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 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.
