This paper explores how effectively building variable air volume air handling unit (VAV AHU) configurations supply required outdoor air to building spaces using data analysis and building performance simulation. One year’s worth of zone-level occupancy data from a floor of an institutional office building was estimated using Wi-Fi device count and concurrent motion detector and CO₂ sensor data. Zone-level ventilation rates were compared to these data to calculate per person ventilation rates. The results indicated that some spaces experienced under-ventilation for up to 34% of occupied hours. For the simulation-based investigation, a 27-zone energy model was used. Occupancy data from the building were used in the simulation. Four operation modes were simulated: default operation, occupancy-based demand-controlled ventilation (DCV), occupancy-based VAV control, and a combination of the latter strategies. The fewest instances of under-ventilation occurred with occupancy-based VAV control. The under-ventilation instances were a result of inefficient distribution of outdoor air across building zones. The supply of standard (10 L/s per person) ventilation with occupancy-based VAV control, instead of a default constant minimum ventilation rate, reduced the number of under-ventilation instances by ∼80% while reducing the energy use by ∼30%.

本文探讨了建筑可变风量空气处理机组 (VAV AHU) 配置如何通过数据分析和建筑性能模拟有效地向建筑空间提供所需的室外空气。_{使用 Wi-Fi 设备数量和并发运动检测器和 CO 2}估算了机构办公楼楼层一年的区域级占用数据传感器数据。将区域级别的通风率与这些数据进行比较，以计算人均通风率。结果表明，一些空间在高达 34% 的占用小时内经历了通风不足。对于基于模拟的调查，使用了 27 区能量模型。模拟中使用了建筑物的占用数据。模拟了四种操作模式：默认操作、基于占用的需求控制通风 (DCV)、基于占用的 VAV 控制以及后一种策略的组合。基于占用率的 VAV 控制发生的通风不足事件最少。通风不足的情况是由于室外空气在建筑区域内分配效率低下的结果。提供标准（每人 10 升/秒）通风和基于占用的 VAV 控制，