基于神经网络的室内环境热舒适度融合算法  

Research on Fusion Algorithm of Indoor Thermal Comfort Based on BP Neural Network

作　　者：张方方[1] 张莲[1] 刘贺[1] 

机构地区：[1]重庆理工大学远程测试与控制技术研究所,重庆400050

出　　处：《重庆工学院学报（自然科学版）》2009年第10期114-118,共5页Journal of Chongqing Institute of Technology

基　　金：重庆市自然科学基金计划重点项目(CSTC;2007BA2023)

摘　　要：针对目前空调机恒定温度传统控制方法的缺陷以及空调机控制技术向热舒适性控制发展的趋势,提出一种基于BP神经网络的室内环境热舒适度融合方法.该方法综合考虑了多个因素对热舒适度PMV指标的影响,应用BP神经网络进行了智能化融合.仿真结果表明:该BP神经网络模型具有良好的性能,能够准确、可靠预测PMV值,并可将其结果应用于室内空调控制系统.Due to the defects of air conditioner＇s traditional control method of constant temperature and the developing trend of air-conditioning control technology of thermal comfort control, a thermal comfort fusion algorithm based on BP neural network is presented. This method considers a number of factors on the thermal comfort index of PMV, and uses BP neural network to do intelligent integration. Simulation results show that the BP neural network model has good performance, is able to accurately and reliably predict PMV value, and the results can be applied to indoor air-conditioning control system.

关 键 词：BP神经网络 热舒适度 PMV指标 融合算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]