Energy consumption and control optimization of ground source heat pump system based on gray-box model

This study made use of the MATLAB software; the gray-box model of the heat pump unit was constructed by using the nonlinear least squares algorithm. The energy consumption model of the chilled water pump was fitted by using the curve fitting toolbox, and the transfer function of the chilled water system was identified through the system identification toolbox. An energy consumption optimization method based on the genetic algorithm was proposed. According to the energy consumption data on August 22nd, the energy consumption, temperature difference, and flow rate before and after optimization are compared. The energy savings at the optimal moment amounted to 17.31%. Meanwhile, the particle swarm optimization (PSO) algorithm is adopted to tune the control parameters of the traditional proportional–integral–derivative (PID); its control effect is better than that of the traditional trial-and-error method. Two groups of temperature difference simulation experiments are designed to verify the PSO–PID-optimized control strategy for the chilled water system based on feedforward. After optimization, the adjustment times of the system are reduced by 76.63% and 75.43%, respectively, demonstrating characteristics of rapidity, accuracy, and stability. In practical applications, this strategy can improve the quality of cooling and heating supply of the ground-source heat pump system and reduce the operating energy consumption.