Research On Intelligent Control Method Of Classroom Environment Quality Based On Model Prediction

The time that modern human activities in the indoor is longer than before,so a good indoor environmental quality is very important to ensure the physical and mental health of indoor personnels,and to promote the quality of production and life of indoor personnels.For students,a healthy and comfortable learning environment is a necessary guarantee students’ health and learning efficiency.The indoor environment quality of a primary school classroom is taken as the research object in this paper,the indoor air temperature and CO₂ concentration is set as the classroom indoor environmental quality indicators according to the characteristics of the target classroom firstly.The basic information of the indoor environment quality indicator datas and the number of personnel are collected,counted and sorted.The RBF neural network modeling method of indoor environment quality is determined,and then the RBF neural network model of indoor environment quality is established based on the collected data to determine weights,the center point and the output of the RBF neural network model.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.Finally,the indoor environment quality prediction control model of target classroom is built on TRNSYS platform,and Matlab module is added in the TRNSYS platform to achieve the prediction of the indoor environment quality control through air conditioning system,the real-time simulation of the control model is carried out,and the performance of the indoor environment quality prediction model based on RBF neural network is verified in the simulation environment.The simulation results show that it is feasible to control the indoor temperature and CO₂ concentration according to the proposed predictive control algorithm based RBFneural network.The method can achieve the rapid control and better stability of indoor environment quality.