  To reach carbon neutrality in the middle of this century, smart controls for
building energy systems are urgently required. Model predictive control (MPC)
demonstrates great potential in improving the performance of heating
ventilation and air-conditioning (HVAC) systems, whereas its wide application
in the building sector is impeded by the considerable manual efforts involved
in setting up the control-oriented model. To facilitate the system
identification (SI) of the building envelope as well as the configuration of
the MPC algorithms with less human intervention, a semantic-assisted control
framework is proposed in this paper. We first integrate different data sources
required by the MPC algorithms such as the building topology, HVAC systems,
sensor data stream and control settings in the form of a knowledge graph and
then employ the data to set up the MPC algorithm automatically. Moreover, an
event-triggered SI scheme is designed, to ensure the computational efficiency
and accuracy of the MPC algorithm simultaneously. The proposed method is
validated via simulations. The results demonstrate the practical relevance and
effectiveness of the proposed semantics-assisted MPC framework with
event-triggered learning of system dynamics.
