Research on the BPNN in the Prediction of PMV

Jian Yao; Jin Xu

doi:10.4028/www.scientific.net/AMM.29-32.2804

Paper Titles

Safety Requirements Model-Based Safety Test Automation of Train Control System of High Speed Railway in China
p.2768

Research on High Strength Aluminum Alloys Components Stress Analysis by Lock-In Thermography
p.2775

Monte Carlo Simulation for the Angle-Dependent Light Transmittance of Thermotropic Material
p.2781

Indoor Thermal Environment Simulation by Using MATLAB and Simulink
p.2785

Design of A GUI Tool for the Prediction of Building Energy Performance
p.2789

Fuzzy Neural Network for Groundwater Level Prediction
p.2794

Daily Discharge Prediction Using Artificial Neural Networks
p.2799

Research on the BPNN in the Prediction of PMV
p.2804

Cause Analysis and the Counter-Policies for Ecological Environment Problems of Coal Resource-Based Cities
p.2809

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 29-32Research on the BPNN in the Prediction of PMV

Research on the BPNN in the Prediction of PMV

Abstract:

In view of the problem that it is difficult to calculate the Fanger’s PMV equation due to its complicated iterative process, a backpropagation neural network (BPNN) model was built to predict PMV. Air temperature, relative humidity, mean radiant temperature, air velocity, metabolic rate and clothing index were used as the input of neural network and PMV output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy absolute error below 5%. As a conclusion, this study has a real signiﬁcance, because it gives a new method with reliability and accuracy in the prediction of PMV.