Intelligent Heating Control Strategies based on Model Predictive Control

With the continuous expansion of urban district heating systems and the increasing complexity of heat demand, it has become a key challenge to enhance energy utilization efficiency and meet the need for comfortable heating. This paper proposes an intelligent heating control strategy based on Model Predictive Control (MPC) to improve the energy utilization efficiency of urban district heating systems and ensure indoor temperature comfort. This strategy leverages the predictive and optimization capabilities of MPC, utilizing a discrete-time statespace model to predict system dynamic behaviours and employing a rolling time-horizon optimization approach to update control inputs in real-time. Within this framework, an adaptive control mechanism is introduced to enable the system to adapt to changes in environment and load, thereby improving temperature control accuracy and energy efficiency. Experimental results demonstrate that, compared with traditional PID and adaptive PID control methods, the MPC control strategy exhibits significant advantages in terms of energy consumption, mean squared error of temperature, and response time. The MPC algorithm effectively reduces energy consumption, enhances temperature control accuracy, and shortens response time under scenarios of sudden temperature changes and demand fluctuations, validating its applicability for optimizing heating systems in complex environments.