Semi-Automated Thermal Envelope Model Setup for Adaptive Model Predictive Control with Event-Triggered System Identification

To reach carbon neutrality in the middle of this cen- tury, smart controls for building energy systems are ur- gently required. Model predictive control (MPC) demon- strates great potential in improving the performance of heating ventilation and air-conditioning (HVAC) systems, whereas its wide application in the building sector is im- peded by the considerable manual efforts involved in set- ting up the control-oriented model. To facilitate the sys- tem identification (SI) of the building envelope as well as the configuration of the MPC algorithms with less hu- man 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 build- ing topology, HVAC systems, sensor data stream and con- trol settings in the form of a knowledge graph and then employ the data to set up the MPC algorithm automat- ically. Moreover, an event-triggered SI scheme is de- signed, to ensure the computational efficiency and ac- curacy of the MPC algorithm simultaneously. The pro- posed 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.