基于模型预测的教室环境品质智能控制方法的研究

Intelligent Control Method of Classroom Environment Quality Based on Model Prediction

现代人类在室内的活动时间越来越长,因此一个良好的室内环境品质能够保证室内人员的身心健康,也能够促进室内人员的生产生活质量。而对于学生来讲,一个健康舒适的学习环境,对于学生的健康和学习效率至关重要。根据该教室的特点确定了目标教室室内温度和CO2浓度为该教室的室内环境品质的指标,并通过实地采集室内环境品质指标数据,根据所采集到的数据,建立教室室内环境品质RBF神经网络模型。然后以教室室内环境品质RBF神经网络模型为基础,建立教室室内环境品质RBF神经网络模型预测控制的数学描述,并设计教室室内环境品质预测控制的控制结构。控制结果表明,结果表明基于RBF神经网络室内环境品质预测控制方法是一种有效的适合于室内环境品质控制的控制方法。

The indoor air temperature and CO2 concentration is set as the classroom indoor environmental quality indicators according to the characteristics of the target classroom,the basic information of the indoor environment quality indicator data and the number of personnel are collected,the RBF neural network model of indoor environment quality is established based on the collected data. After that,the predictive control method of RBF neural network is determine to establish indoor environmental quality RBF neural network prediction model and to design controller of indoor environmental quality prediction.