Study On Control Mode Of Low-temperature Floor Heating System

Low temperature radiation heating technology has a good comfort and energy saving advantages,in the field of building heating is more and more attention and is widely used.Due to low temperature floor radiant heating system required water supply temperature,and ground source heat pump,water source heat pump can provide almost the same heating temperature,low temperature floor radiant heating system is the use of soil heat,solar and other low-grade heat source of the most suitable end TheThe control part is an important part of the heating technology,because the floor radiant heating system has its own characteristics,namely,heat storage,the traditional control method will generally reduce the control accuracy of the system,within the control range to control the temperature of the work area is difficult to control,Will reduce the thermal comfort,increase energy consumption.Therefore,this paper uses low-grade heat source of low-temperature floor radiant heating system as the object of study,based on artificial neural network to establish the system of predictive control model.This paper chooses a rural independent house in Qingdao as an experimental platform,and has carried out more than one month of experimental research since the date of heating.The main purpose is to obtain and analyze the experimental data and determine the input and output parameters of the system predictive control model.In order to analyze the control effect of conventional control mode,TRNSYS software was used to study the indoor temperature feedback control of low temperature floor radiant heating system.The influence of thermal hysteresis on the indoor heating was analyzed and its control effect was analyzed.The control effect is poor.The single-step predictive control model of low-temperature floor radiant heating system based on BP network is established,which proves that the model is feasible.At the same time,a predictive control model of low temperature floor radiant heating system based on RBF network is established.The BP network is better than the RBF network.Using the artificial neural network control mode,has a good control accuracy.Can be based on the predicted next time in the indoor temperature control system in the heat pump unit start and stop,to overcome the indoor temperature due to thermal delay caused by adverse effects,can make the indoor temperature control in the set temperature range,saving energy,and enhanced The indoor thermal comfort.