Application of Gaussian Process Regression to Prediction of Thermal Comfort Index
In this paper, the theory of Gaussian Process Regres sion (GPR) was introduced, and the Gaussian Process Regres sion model was established to predict thermal comfort index.In this model, parameters of activity level, clothing insulation, air temperature, air relative humidity, air velocity and mean radiant temperature were selected as the input vectors, and PMV index was the output vector.The calculated results indicated that the Gaussian Process Regression model had good agreement with those of Fangers equation.Furthermore, the results of the Gaussian Process Regression model, the BP neural network model and SVM were compared and analyzed, it was concluded that the GP model had relatively higher fitting precision and generalization adaptability.With this model, the requirements of real-time control with PMV index as a controlledparameter in an air-conditioning system could be satisfied.
Gaussian Process Regression thermal comfort PMK
Sun Bin Yan Wenlai
School of Energy and Power Engineering,Northeast Dianli University,Jilin 132012,China
国际会议
哈尔滨
英文
982-985
2013-08-16（万方平台首次上网日期，不代表论文的发表时间）