A comprehensive evaluation method of heat source tower heat pump applicability considering regional climate differences

Abstract

The heat source tower heat pump system is widely used in large and medium-sized air conditioning systems due to its good energy-saving advantages. However, there is no relatively reasonable evaluation system for the applicability of heat source tower heat pumps due to significant regional climate differences. Therefore, in order to better comprehensively evaluate the applicability of the heat source tower heat pump system, a comprehensive evaluation index system for the applicability of the heat source tower heat pump system was first constructed. On the basis of this evaluation index, an applicability evaluation model based on backpropagation neural network is constructed. In response to the slow convergence speed and susceptibility to local values in the application process of this evaluation model, particle swarm optimization algorithm is used to improve it. A comprehensive evaluation model for the applicability of heat source tower heat pumps based on improved backpropagation neural networks has been constructed. For the evaluation model constructed in the study, experimental data from four different regions were selected for validation. The experimental results show that in the training set, the F-Measure value of the evaluation model reaches 0.949, and in the test set, the F-Measure value of the model reaches 0.973. The comprehensive evaluation data from four regions indicate that the heat source tower heat pump system can achieve different heating and cooling effects in different regions. This indicates that the proposed comprehensive evaluation model for the applicability of heat source tower heat pumps based on this improved method has good evaluation results. It can conduct a good analysis of the applicability of the heat source tower heat pump system, providing effective support for developing reasonable and energy-saving refrigeration and heating methods in different regions.