A control-oriented hybrid model for a direct expansion air conditioning system

作者：Xu, Xiangguo^*; Huang, Zhiyuan; Wang, Xiaofei; Zhong, Ziwen; Zhang, Xiaobo

来源：Energy and Buildings, 2017, 155: 76-87.

DOI：10.1016/j.enbuild.2017.09.004

• Air conditioning
• Direct expansion
• Control oriented
• Empirical modeling
• Variable speed
• Humidity control

Indoor air humidity is an important parameter for building up a thermally appropriate artificial indoor environment. However, introducing air humidity as a controlled parameter in addition to air temperature would significantly increase the difficulty to develop a control-oriented model for building air conditioning systems. This is particularly true for direct expansion (DX) air conditioning (A/C) systems, whose operational parameters are highly coupled and behave non-linearly and influenced by the controlled parameters. Neither physical modeling approach nor artificial neural network (ANN) modeling approach could solely satisfy the requirement, in terms of accuracy and sensitivity, for simultaneous control of air temperature and humidity using a DX A/C system. In this paper, a hybrid modeling approach is proposed and validated, which uses the physical modeling approach to simulate the performance of evaporator for accurately catching the cooling and dehumidification processes under various working conditions and uses ANN to simulate all other components of a DX A/C system for reduced calculation efforts. By such a hybrid modeling approach, the advantage of simplicity of an ANN-based sub-model could be utilized and the disadvantage of it that do not allow to accurately eXtrapolate beyond the range of the data used for training/estimating the model parameters could be avoided.

• 出版日期2017-11-15
• 单位浙江大学