基于双变量调节的室内动态热舒适度控制系统研究  

Research on Indoor Dynamic Thermal Comfort Control System Based on Bi-Variable Regulation

作　　者：黄昌琴[1] 刘莎 丁思发 高岩松 HUANG Changqin;LIU Sha;DING Sifa;GAO Yansong(Yang-En University,Quanzhou 362000,China;Putian University,Putian 351100,China)

机构地区：[1]仰恩大学,泉州362000 [2]莆田学院,莆田351100

出　　处：《机电技术》2024年第6期32-37,78,共7页Mechanical & Electrical Technology

基　　金：福建省中青年教师教育科研项目(JAT210497)。

摘　　要：现有的室内热舒适度控制方法多侧重于恒温控制,但长期处于恒温且空气不流通的环境中易引起人体不适,甚至引发健康问题。文章提出设计了一种基于双变量调节的动态PMV(Predicted Mean Vote,预测平均投票值,衡量人体热舒适度的指标)热舒适度控制系统。该系统利用BP神经网络模型,基于环境参数、人体活动和服装热阻预测PMV值,在变温恒风速控制和变温变风速控制下进行仿真试验,研究了PMV的变化与室内温度和空气流速之间的关系,通过智能算法同时调节空调温度和风速,实现了PMV值在期望范围内的有效控制。试验结果验证了该系统的高精度控制能力和强抗干扰性。Existing indoor thermal comfort control methods mostly focus on constant temperature control,but long-term exposure to an environment with constant temperature and poor air circulation can easily cause human discomfort and even cause health problems.The article proposes the design of a dynamic PMV(Predicted Mean Vote,an indicator of human thermal comfort)thermal comfort control system based on dual-variable adjustment.The system uses the BP neural network model to predict the PMV value based on environmental parameters,human activities and clothing thermal resistance.It conducts simulation tests under variable temperature constant wind speed control and variable temperature variable wind speed control to study the relationship between changes in PMV and indoor temperature and air flow rate.relationship.Through intelligent algorithms to simultaneously adjust the air conditioning temperature and wind speed,effective control of the PMV value within the desired range is achieved.The test results verified the system's high-precision control capabilities and strong anti-interference performance.

关 键 词：热舒适度 动态PMV 双变量调节 神经网络 

分 类 号：TU831.3[建筑科学—供热、供燃气、通风及空调工程] TP273[自动化与计算机技术—检测技术与自动化装置]