In variable refrigerant flow (VRF) systems with multiple indoor units, individual energy metering (IEM) not only contributes to a fair charge for the tenants, thus avoiding their complaints on the charging fee, but also shows the actual allocation of capacity for IUs. However, it is extremely challenging to measure the accurate capacity of each IU directly according to current researches though total capacity can be measured. In this paper, methods of individual energy metering for VRF systems were reviewed, and three new methods, based on the electronic expansion valve (EEV), machine learning model (MLM), and throttling model (TM), were proposed. The accuracy and applicability of the three methods were investigated and analyzed according to experiments on a water-cooled VRF system. In the experiments, the maximum deviation factors of the EEV-, MLM-, and TM-based methods were ±7.8%, ±6.0%, and ±6.9%, respectively. In addition, coefficients of variation of the root-mean-square error of the above methods were ±4.4%, ±3.3%, and ±4.4%, respectively. Considering the practicability and feasibility in real projects, the TM-based method was applied to an air-cooled VRF system, showing superior accuracy in cooling conditions.

在多台室内机的可变制冷剂流量（VRF）系统中，单独的能量计量（IEM）不仅有助于为租户公平收费，从而避免他们对收费费用的抱怨，而且还显示了IU的实际容量分配。然而，尽管可以测量总容量，但根据当前的研究直接测量每个 IU 的准确容量极具挑战性。本文回顾了 VRF 系统的个体能量计量方法，并提出了基于电子膨胀阀 (EEV)、机器学习模型 (MLM) 和节流模型 (TM) 的三种新方法。通过在水冷 VRF 系统上的实验，对三种方法的准确性和适用性进行了调查和分析。在实验中，EEV-的最大偏差因子，基于 MLM 和 TM 的方法分别为 ±7.8%、±6.0% 和 ±6.9%。此外，上述方法的均方根误差的变异系数分别为±4.4%、±3.3%和±4.4%。考虑到实际项目的实用性和可行性，将基于TM的方法应用于风冷VRF系统，在冷却条件下表现出优异的精度。