Demonstration of an MPC framework for all-air systems in non-residential buildings

Abstract

In educational buildings, the HVAC system is a major contributor to the total energy use. It is well-known that energy reductions could be achieved by more advanced control strategies for HVAC systems. A model predictive control (MPC) strategy could be a viable solution. Contrary to the standard rule based control (RBC), the predictive control can adjust in the supply air temperature, airflow rate prior to the start of a lecture. This study aims to develop and demonstrate an MPC framework for an all-air system that controls both the room temperature and the CO2-concentrations. To predict these variables two model identification techniques were compared: auto-regressive with exogenous input (ARX-MPC) linear model and a grey-box resistor–capacitor nonlinear model (RC-NMPC). In addition, a cost function that minimized the energy use while guaranteeing thermal comfort and indoor air quality (IAQ) was formulated. This study showed that the ARX-MPC was preferred over the RC-NMPC given its simplicity. As for the model identification, the ARX model was less computationally time-consuming than the grey-box model. Furthermore, the RC-NMPC required more computational time than the ARX-MPC, since the RC-NMPC required a non-linear solver while the ARX-MPC used a linear one. The developed MPC framework was than implemented through BACnet in a real-use educational building to test its actual performance. The energy savings calculated for the implemented MPC strategies with an occupancy based heating set-point were respectively 10%–40% for electric energy use and 21%–55% for thermal energy use.