Indoor Thermal Comfort PMV Index Prediction Based on Particle Swarm Algorithm and Least Square Support Vector Machine

Abstract

The prediction model of indoor thermal comfort PMV index based on least squares support vector machine (LS-SVM) is established by using the nonlinear relationship between human thermal comfort and its influencing factors and the characteristic that particle swarm has of fast global optimization. Adopting the parameters of least squares support vector machine optimized by Particle Swarm algorithm, the mapping relations between the six factors including indoor air temperature, relative humidity, air velocity, mean radiant temperature, human metabolic rate, thermal resistance and PMV index can be formed through the sample data learning. The experimental results show that the method is accurate and effective.