低温地板辐射供暖系统的控制方式研究

发布时间：2018-04-21 06:58

本文选题：低温地板辐射采暖 + 人工神经网络　；参考：《青岛理工大学》2017年硕士论文

【摘要】：低温辐射采暖技术具有着良好的舒适和节能等优点,在建筑采暖领域正越来越多得受到重视并被广泛应用。由于低温地板辐射供暖系统所要求的供水温度,与地源热泵、水源热泵其能提供的供热温度差不多,低温地板辐射供暖系统是是利用土壤中的热能、太阳能等低品位热源的最适用末端。控制环节是供暖技术的重要部分,因为地板辐射采暖系统具有其自身的特点,即蓄热性,传统的控制方法一般会降低系统的控制精度,在设定范围内控制工作区域的温度难以控制,会降低热舒适度,增加能耗。所以本文以使用低品位热源的低温地板辐射供暖系统作为研究的对象,基于人工神经网络建立系统的预测控制模型。本课题选取了青岛地区某一农村独立住宅作为实验平台,自采暖日起进行了一个多月的实验研究,主要的目的是:获取并分析了实验数据,确定系统预测控制模型的输入及输出参数。为分析常规控制方式的控制效果,使用TRNSYS软件对低温地板辐射供暖系统进行室内温度反馈控制的研究,分析辐射地板的热滞后性对室内供暖的影响,分析其控制效果。得出其控制效果不佳。建立了基于BP网络的低温地板辐射供暖系统单步预测控制模型,证明该模型是可行的,同时也建立了基于RBF网络的低温地板辐射供暖系统的预测控制模型,对两种控制方式进行了比较分析,BP网络优于RBF网络。采用人工神经网络的控制方式,具有着良好的控制精度。可以根据预测的下一个时刻的室内温度控制系统里热泵机组的启停,克服室内温度由于热延迟而产生的不利影响,都可使室内温度基本控制在设定温度范围内,节约能源,且增强了室内热舒适性。
[Abstract]:Low temperature radiation heating technology has the advantages of good comfort and energy saving, and has been paid more and more attention to and widely used in the field of building heating. Because the water supply temperature required by the low temperature floor radiation heating system is about the same as that of the ground source heat pump, the water source heat pump can provide the same heating temperature. The low temperature floor radiant heating system uses the heat energy in the soil. The most suitable end of low grade heat source such as solar energy. The control link is an important part of the heating technology, because the floor radiation heating system has its own characteristics, that is, heat storage, the traditional control method will generally reduce the control accuracy of the system. It is difficult to control the temperature of the working area in the set range, which will reduce the thermal comfort and increase the energy consumption. So this paper takes the low temperature floor radiant heating system with low grade heat source as the research object and establishes the predictive control model of the system based on artificial neural network. This subject selects a rural independent house in Qingdao area as the experimental platform, has carried on the experimental research for more than one month since the heating day, the main purpose is: has obtained and analyzed the experimental data, The input and output parameters of the predictive control model are determined. In order to analyze the control effect of conventional control method, the indoor temperature feedback control of low temperature floor radiant heating system is studied by using TRNSYS software. The effect of thermal lag of radiant floor on indoor heating is analyzed and its control effect is analyzed. It is concluded that the control effect is not good. The single step predictive control model of low temperature floor radiation heating system based on BP network is established, which proves that the model is feasible. At the same time, the predictive control model of low temperature floor radiation heating system based on RBF network is also established. The two control methods are compared and analyzed. The BP network is superior to the RBF network. The control method of artificial neural network has good control precision. According to the predicted start and stop of the heat pump unit in the indoor temperature control system at the next moment, to overcome the adverse effect of the indoor temperature due to the thermal delay, the indoor temperature can be basically controlled within the set temperature range, thus saving energy. And enhanced indoor thermal comfort.
【学位授予单位】：青岛理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TU832.1

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