基于Elman神经网络的PMV参数预测建模

PMV Parameter Prediction and Modeling Based on Elman Neural Network

传统PMV指标计算方法具有复杂度高、延时大的缺陷.根据PMV参数的时变特征,利用Elman神经网络建立PMV参数预测模型,实现对热舒适度的在线监测.模型以温度、相对湿度、风速和平均辐射温度为输入,以PMV指标为预测输出,具有良好的泛化能力.仿真结果表明该方法的预测结果与数值计算的结果相近,同时训练后神经网络的计算时间优于传统方法的计算时间.

The traditional numerical calculation method of PMV has the defects of high computational complexity and large time delay.In this paper,according to the time-varying characteristic of PMV index,PMV prediction model is established based on Elman neural network and the on-line monitoring of thermal comfort is realized.The temperature,air velocity,relative humidity and mean radiant temperature are selected as the inputs of the prediction model and the PMV value is assigned as output.The prediction model has good generalization capacity.Simulation results show that the predictive results of the proposed method are in agreement with the results of numerical calculation;meanwhile the computation time of the proposed method is superior to that of the traditional method after the Elman neural network is trained sufficiently.