Prediction of PMV of Body Thermal Comfort in Office Based on PSO-GRNN

Because the prediction of thermal comfort is a complex, nonlinear process, which is not convenient for the application of real-time control of air conditioning, neural network can quickly establish the nonlinear mapping between the input and output of PMV index, which becomes a new research direction of PMV index solution. PSO is used to optimize GRNN parameters and build a prediction model, which can find the optimal smooth factor of GRNN network in the shortest time, so that the trained network can meet the requirements of both learning ability and learning speed. The results show that the generalized regression neural network inherits all the advantages of RBF network, and is superior in approximation ability and learning speed. From the experiment, when the SPREAD value is the same, the prediction accuracy of GRNN network is slightly better than that of RBF network. The prediction accuracy of GRNN network can also be improved obviously after PSO optimization.