考虑学习效率的教室内热环境调控系统研究

Thermal Climate Control Systems in Classrooms Considering Learning Efficiency

教室环境对学生的舒适程度和学习效率有直接影响,因此如何对其进行调控非常重要。针对现有室内调控方案未考虑学习效率的问题,在确定热舒适度与学习效率量化关系的情况下,提出一套针对教室的室内热环境调控系统。将学习效率最高时的预测平均投票值(predicted mean vote,PMV)作为系统的目标,为实时获取PMV提出了基于粒子群优化的BPNN(back propagation neural network)模型对其进行预测的方法,通过PMV反推出热环境参数(温度、风速和相对湿度)的目标值,并提出基于BPNN的比例积分微分(proportion integration differentiation,PID)控制器对其进行调控以改变室内热环境。通过实地采集的数据对调控系统进行仿真模拟,结果证明了调控系统的可行性和有效性,为积极推进教室内热环境的调控提供了有效依据。

The classroom environment has a direct impact on student comfort and learning efficiency,so it is important to know how to regulate it.To address the problem that existing indoor regulation schemes did not take learning efficiency into account,an indoor thermal environment regulation system for classrooms was proposed,based on a quantitative relationship between thermal comfort and learning efficiency.The predicted mean vote(PMV)at the highest learning efficiency was taken as the target of the system,and the method of prediction was proposed based on a back propagation neural network(BPNN)model with particle swarm optimisation to obtain the PMV in real time,and the thermal environment parameters(temperature,air speed and relative humidity)were inferred from the PMV.The target values of the thermal environment parameters(temperature,air speed and relative humidity)were inferred from the PMV,and a BPNN-based proportion integration differentiation(PID)controller was proposed to regulate them to change the indoor thermal environment.The simulation of the control system with data collected in the field was demonstrated the feasibility and effectiveness of the control system,providing an effective basis for actively promoting the control of the thermal environment in the classroom.