Multiple operating condition intelligent regulation strategy and experimental platform for chillers of large buildings based on multiple model adaptive predictive control

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in buildings, with the chiller—the core component of the system—playing a vital role in meeting cooling demands by carrying heat. Therefore, flexible demand-based regulation of the chiller is essential to improve building thermal comfort and reduce energy consumption. As a nonlinear, highly coupled, and dynamic system, the chiller exhibits varying system characteristics under different operating points and environmental conditions. This necessitates a control strategy capable of adapting to diverse operating scenarios.

To address the challenge of multicondition chiller regulation in HVAC systems, an intelligent regulation method based on multiple model adaptive predictive control (MMAPC) was proposed. To analyze the dynamic characteristics of chiller under different operating conditions, its working mechanism was examined using thermodynamic theory, and a chiller control model suitable for real-time operations was established. Based on the influence of environmental factors, three representative operating conditions were identified and classified. For each condition, an incremental model predictive controller was designed using the mechanism-based model. These controllers were integrated through an adaptive weighted control variable fusion approach to form the overall MMAPC strategy. To evaluate the proposed approach, a real-time experimental platform was developed, comprising an intelligent chiller regulation unit, a supervisory computer with a large display screen, and several underlying control devices. The platform supports flexible communication configuration, high-volume data processing, and the deployment of various intelligent algorithms. Comparative experiments between single MPC control and the proposed MMAPC strategy were conducted on this platform.

Experimental results showed that the proposed MMAPC approach reduced the average tracking error by 70% compared with single MPC control. Additionally, it decreased the average overshoot between different operating conditions by approximately 75% and reduced the average standard deviation of the compressor valve opening by around 91%. These results demonstrated the feasibility and effectiveness of the proposed control strategy in achieving accurate chiller outlet temperature tracking while maintaining HVAC system stability. The developed experimental platform successfully enabled real-time data acquisition, strategy computation, and command issuance, while visually displaying system status on the large screen.

The intelligent experimental platform effectively supports real-time strategy verification and provides a practical foundation for teaching and research. The MMAPC strategy demonstrates excellent performance in multicondition chiller regulation and show strong potential for solving tracking control problems in dynamic, time-varying systems. This method lays the groundwork for deploying chiller operation optimization algorithms and contributes to energy-saving and emission-reduction goals under stable HVAC operation.