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作 者:高喜玲[1] 商利斌[1] 夏如杰 张晓冬 GAO Xi-ling;SHANG Li-bin;XIA Ru-jie;ZHANG Xiao-dong(Jiangsu Vocational Institute of Architectural Technology;Hubei Design Branch of China Architectural Technology Design Group Co.,Ltd)
机构地区:[1]江苏建筑职业技术学院 [2]中国建筑技术设计集团有限公司湖北设计分院
出 处:《建筑热能通风空调》2022年第2期18-21,83,共5页Building Energy & Environment
基 金:江苏建筑职业技术学院2021年科研项目(JYA320-11);江苏省住建厅科学技术项目(2019ZD001149)。
摘 要:为了研究直膨式(DX)空调系统室内温度和相对湿度的控制策略,详细说明了为开发基于人工神经网络的动态模型而进行实验的实验条件。介绍了该实验系统动态模型的建立,考虑了输出空气温度和相对湿度之间的耦合效应。探讨在固定的室内显热负荷和潜热负荷下,将输出空气的干球温度和湿球温度与其压缩机和送风机转速的变化联系起来,通过比较实验DX空调系统在压缩机和送风机转速发生阶跃变化后的开环响应测量值与使用所开发的动态模型的相应预测结果,对该动态模型进行了实验验证。验证结果表明,所开发的动态模型具有较高的预测精度,有助于更好地理解DX空调系统的动态运行性能,并有助于开发基于人工神经网络的控制器来同时控制室内空气温度和湿度。In order to study the control strategy of indoor temperature and relative humidity of direct expansion(DX)air conditioning system,the experimental conditions for developing a dynamic model based on artificial neural network are described in detail?The establishment of the dynamic model of the experimental system is introduced,and the coupling effect between output air temperature and relative humidity is considered?Under fixed indoor sensible heat load and latent heat load,the dry bulb temperature and wet bulb temperature of output air are connected with the changes of compressor and forced draft fan speed.By comparing the measured open-loop response of DX air conditioning system after the step change of compressor and forced draft fan speed with the corresponding prediction results of the developed dynamic model,The dynamic model is verified by experiments.The verification results show that the developed dynamic model has high prediction accuracy,helps to better understand the dynamic operation performance of DX air conditioning system,and helps to develop a controller based on artificial neural network to control indoor air temperature and humidity at the same time.
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