Heating, Ventilation, and Air Conditioning (HVAC) Temperature and Humidity Control Optimization Based on Large Language Models (LLMs)

Heating, Ventilation, and Air Conditioning (HVAC) systems primarily consist of pre-cooling air handling units (PAUs), air handling units (AHUs), and air ducts. Existing HVAC control methods, such as Proportional–Integral–Derivative (PID) control or Model Predictive Control (MPC), face limitations in understanding high-level information, handling rare events, and optimizing control decisions. Therefore, to address the various challenges in temperature and humidity control, a more sophisticated control approach is required to make high-level decisions and coordinate the operation of HVAC components. This paper utilizes Large Language Models (LLMs) as a core component for interpreting complex operational scenarios and making high-level decisions. A chain-of-thought mechanism is designed to enable comprehensive reasoning through LLMs, and an algorithm is developed to convert LLM decisions into executable HVAC control commands. This approach leverages adaptive guidance through parameter matrices to seamlessly integrate LLMs with underlying MPC controllers. Simulated experimental results demonstrate that the improved control strategy, optimized through LLM-enhanced Model Predictive Control (MPC), significantly enhances the energy efficiency and stability of HVAC system control. During the summer conditions, energy consumption is reduced by 33.3% compared to the on–off control strategy and by 6.7% relative to the conventional low-level MPC strategy. Additionally, during the system startup phase, energy consumption is slightly reduced by approximately 17.1% compared to the on–off control strategy. Moreover, the proposed method achieves superior temperature stability, with the mean squared error (MSE) reduced by approximately 35% compared to MPC and by 45% relative to on–off control.