Hybrid steady-state modeling of a residential air-conditioner system using neural network component models

作者：Shao, Liang-Liang; Yang, Liang; Zhao, Ling-Xiao; Zhang, Chun-Lu^*

来源：Energy and Buildings, 2012, 50: 189-195.

DOI：10.1016/j.enbuild.2012.03.036

• Air-conditioning system
• Model
• Neural network

This paper presents a new hybrid steady-state modeling approach for air-conditioning systems. At the system level, it follows the first principles to keep the conservation of mass, energy, and momentum, respectively. At the component level, the physics-based component models are entirely or partially replaced with neural networks. Component neural networks can be trained using the data from laboratory or from the well-validated physics-based component models. Numerical comparisons between the system model consisting of component neural networks and that consisting of physics-based component models indicate that the two system modeling approaches give very close predictions in a wide range of operating conditions. With the proposed hybrid modeling approach, robustness and speed of system modeling can be significantly improved.

• 出版日期2012-7
• 单位同济大学; 上海交通大学