Occupancy-Driven Demand Controlled Ventilation in Office Buildings

Demand controlled ventilation adjusts outdoor air supply according to real-time occupancy, but its practical benefit depends on the accuracy of occupancy estimation. This study evaluates a low-intrusive multi-sensor occupancy estimation method and quantifies its energy and indoor air quality implications in a real office building.

研究背景

需求控制通风依据实时人员在室情况调节新风量，其节能效果取决于在室率估计的准确性。传统 CO₂ 浓度法存在滞后与累积误差问题。

方法

提出一种低侵入式的多源传感融合估计方法，融合 CO₂ 浓度、门磁与 Wi-Fi 探针数据，采用状态空间模型估计实时在室人数。

结论

在某办公楼为期三个月的现场测试中，所提方法的在室人数估计平均绝对误差约为 1.8 人；据此实施需求控制通风后，新风处理能耗下降约 19%，室内 CO₂ 浓度峰值维持在标准限值以内。

@article{Q.Feng20242024-occ,
  title   = {Occupancy-Driven Demand Controlled Ventilation in Office Buildings},
  author  = {Q. Feng and S. He},
  journal = {Building and Environment},
  year    = {2024},
  doi     = {10.1016/j.buildenv.2024.100007},
}