使用来自建筑能源管理系统（BEMS）的趋势数据是一种经济高效的解决方案，可以为正在进行的调试提供必要的数据。本文介绍了三种不同的虚拟空气流量计的使用，以及每15分钟记录的趋势数据，用于估算空气处理机组引入的室外空气流量。在没有物理传感器的供暖、通风和空调系统中，虚拟流量计估计物理变量的值。为此，使用数学模型以及系统中可用传感器的测量值。在本研究中，结果表示为室外空气流量与送风流量的比值α。 将三个数学模型应用于混合箱，以计算α:（a）简化的能量平衡方程和空气温度测量值，（b）空气质量、水质量和能量平衡方程和空气温度和相对湿度测量值，以及（c）能量平衡方程和空气温度和相对湿度测量值。还估计了测量误差引起的不确定度传播。案例研究大楼是蒙特利尔一所大学的新研究中心。本文介绍了2014年4月7日至5月12日的结果。对于每个数学模型，开发了一个回归模型，供虚拟流量计使用。基于a和b的回归模型给出了R²=0的良好估计。 97，具有验证数据集；而基于c的回归模型在R²=0.19时性能较低。由于误差通过详细方程传播，第二种和第三种情况下估计的不确定性要高得多。基于a的回归模型是虚拟空气流量计的最佳选择。引用:2016年冬季会议，佛罗里达州奥兰多，会议论文

The use of trend data from Building Energy Management Systems (BEMS) is a cost-effective solution to provide the necessary data for ongoing commissioning. This paper presents the use of three different virtual air flow meters, along with trend data, recorded every 15 minutes for the estimation of the outdoor air flow rate brought in the air-handling units. A virtual flow meter estimates the value of a physical variable in the heating, ventilating and air-conditioning system where a physical sensor does not exist. For this purpose, a mathematical model is used along with measurements from available sensors in the system. In this study, the results are presented as the ratio α of the outdoor air flow rate to the supply air flow rate. Three mathematical models are applied to the mixing box to calculate α: (a) a simplified energy balance equation along with air temperature measurements, (b) the air mass, water mass and energy balance equations along with measurements of air temperature and relative humidity, and (c) the energy balance equation along with measurements of air temperature and relative humidity. The uncertainty propagation due to measurement errors is also estimated. The case study building is a new research center of a university in Montreal. The paper presents results from April 7 to May 12, 2014. For each mathematical model a regression model is developed to be used by the virtual flow meter. The regression models based on a and b give good estimates with R2= 0.97, with the verification data set; while the regression model based on c has low performance with R2= 0.19. Due to the error propagation through the detailed equations, the uncertainty of estimates is much higher in the second and third case. The regression model based on a is the best candidate for the virtual air flow meter.