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《Building Science》 2011-08
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Application of Least Squares Support Vector Machine to Predict Thermal Comfort Index

SUN Bin,JIANG Neng-fei,XUE Guang-xin,LIU Guang-lin,QIAN Zheng,CHEN Jun(School of Energy and Power Engineering,Northeast Dianli University,Jilin 132012,China)  
In this paper,the theory of least square-support vector machine(LS-SVM)was introduced,and the LS-SVM 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 LS-SVM model had good agreement with those of Fanger's equation.Furthermore,the results of the LS-SVM model,the BP neural network model and the traditional support vector machine(SVM) model were compared and analyzed,it was concluded that the LS-SVM model had relatively higher fitting precision and generalization capability.With this model,the requirements of real-time control with PMV index as a controlled parameter in an air-conditioning system could be satisfied.
【CateGory Index】: TP18;TU111
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