A comprehensive review of predictive control strategies in heating, ventilation, and air-conditioning (HVAC): Model-free VS model

Predictive control offers significant advantages in nonlinear control, high thermal inertia, and dynamic control. This article uses a Systematic Reviews and Meta-Analyses methodology to review 245 studies on predictive control in HVAC systems over the past 12 years, focusing on Model Predictive Control (MPC) and Model-Free Predictive Control (MFPC). In cooling systems, MPC is widely applied to energy efficiency management, continuous operation and maintenance, and overall system optimization in multi-zone residences. Its advantage is its ability to respond to system dynamics and precisely control key components such as cooling towers, condensers, evaporators, and pumps. Research focuses on simplifying models, reducing computational complexity, and enhancing real-time performance. In contrast, MFPC saves energy in equipment components and overall operation through intelligent valves, agent control programs, and other methods. Research focuses on developing new reinforcement learning algorithms to improve control efficiency and reliability. MPC research in heating systems focuses on hydraulic and thermal balance in central heating systems and expands to managing renewable energy hybrid systems. The research aims to dynamically adjust to meet user thermal comfort requirements while reducing energy consumption and improving efficiency. Key technologies include modeling techniques, distributed MPC, cross-regional integrated control, and efficient renewable energy integration strategies. MFPC precisely controls heating system water supply temperature, heat pump energy efficiency, and heating terminals through model-free algorithms like deep reinforcement learning and multivariable extremum seeking control. In integrated HVAC systems, MPC research focuses on managing multi-energy systems through hierarchical decomposition and multi-layer strategies, seamless renewable energy integration and optimization, and developing multi-objective optimization and decision support tools. MFPC research includes automatic grading strategies for integrated controllers, online optimization balancing methods, multi-agent methods, and developing intelligent model-free adaptive control strategies. However, MFPC integration in practical applications still needs strengthening. This review guides researchers in selecting the best predictive control mode for various HVAC system applications.