Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort

We proposed a novel multi-objective model predictive control (MPC) approach based on a straightforward internal prediction model to achieve building energy efficiency and maintain the indoor temperature within a predetermined comfort range. Using the CARNOT Toolbox, we built a detailed room model based on a real room with water-circulated radiator heating. We developed an MPC controller using MATLAB and combined it with the room model in the CARNOT Toolbox to tune the controller parameters and evaluate its performance. Based on the co-simulations, a control step of 15 min and a prediction horizon of 90 min were found to be suitable for room level indoor thermal comfort control. The performance of the controller was evaluated in terms of multiple criteria, including control accuracy, hydrodynamic stability, and energy consumption. Compared with the traditional proportional-integral-derivative (PID) control, the MPC demonstrated a 16.4 % improvement in control accuracy, 2.8 % lower energy consumption, and a 50 % reduction in the hot water flow change rate, improving the system’s hydrodynamic stability. A significant advantage of the MPC is that it is possible to compute different efficient solutions by modifying the parameters, among which the decision-makers can choose their most preferred compromise solution considering multiple criteria.