Existing studies have treated variable refrigerant flow (VRF) control as a local control problem where control variables are determined using only local state information. This study investigates an integrated VRF control in which the VRF control actions are determined based on not only local information but also the dynamics of the room it serves. For this purpose, two artificial neural network simulation models were developed: one to predict indoor air temperature of the room and the other to predict the VRF’s compressor power. The ANN simulation models were validated with 192 experiments conducted in an experimental chamber. The results revealed that the integrated control reduced cooling and compressor energy use of the VRF by 21.6% and 13.1%, respectively, compared to the local control. These energy savings were achieved because the integrated control ANN models were aware of the dynamic relationship between the VRF and the target room.

现有研究已将可变制冷剂流量（VRF）控制视为局部控制问题，其中仅使用局部状态信息确定控制变量。这项研究调查了一个集成的VRF控制，其中VRF控制动作不仅基于本地信息而且还基于它所服务的房间的动态来确定。为此，开发了两种人工神经网络仿真模型：一种用于预测房间的室内空气温度，另一种用于预测VRF的压缩机功率。在实验室内进行的192次实验验证了ANN模拟模型的有效性。结果表明，与本地控制相比，集成控制分别将VRF的冷却和压缩机能耗降低了21.6％和13.1％。