Research on thermal comfort optimization in hospital waiting rooms based on neural network-particle swarm optimization
LI Pengfei
WANG Ke
SONG Yongxing
LIANG Chen
ZHANG Chi
LIU Jizhou
Abstract：In response to the issue that some hospital waiting rooms fail to meet the thermal comfort requirements of the human body,Computational Fluid Dynamics(CFD)software was adopted to simulate the temperature field and velocity flow field of the waiting area in a certain hospital in Jinan under different supply air parameters(including supply air temperature and supply air velocity),and the accuracy of the simulation model was verified through measured data.By using the iterative calculation of the predicted average voting(PMV),a database containing supply air parameters and PMV values of 34 positions in the waiting area space was constructed,and an algorithm based on neural network and particle swarm optimization(NN-PSO)was proposed to calculate the optimal combination of supply air parameters corresponding to different objective functions.The results show that the supply air temperature and speed are the key factors affecting the PMV index in the waiting room.The NN-PSO algorithm can complete the search for the optimal combination of air supply parameters within 90 seconds,effectively reducing the energy consumption of the air conditioning system.
Keywords：air supply parametersfeedforward neural networkparticle swarm optimization algorithm
Publication Date：2025-09-25
Online Publishing Date：2025-11-10(First online date of this platform, not the publication date of the document)
Pages：6( 17-22 )