Model predictive control optimization for rapid response and energy efficiency based on the state-space model of a radiant floor heating system

Abstract

In recent years, radiant floor heating systems have been favored by more and more consumers because of their better comfort, high stability, and energy efficiency. This is especially true in southern China, where heating demand is increasing, and its application is becoming more common. This study explored the method of establishing a state-space model of a variable-flow radiant heating system without selecting and applying extensive measurement data and the application of the model predictive control method to its control optimization. The average errors between the state-space model and the experimental data in terms of the zone air temperatures and radiant floor surface temperatures were only −0.21–0.07 °C. The state-space model saves 76%~95% of the computation time compared to the Trnsys model, particularly suitable for large volumes and longtime building simulations. For the intermittent operation of the radiant floor heating with an air-source heat pump, the MPC controller reduced the response time by about 90 min compared with that with the PID controller, a reduction of approximately 56%. Besides, the MPC controller can effectively reduce energy consumption by 14.9% and improve the COP of the air-source heat pump by 24.5% compared with the PID controller.