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Inferring occupant counts from Wi-Fi data in buildings through machine learning

机译：通过机器学习从建筑物中的Wi-Fi数据推断人数

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An important approach to curtail building energy consumption is to optimize building control based on occupancy information. Various studies proposed to estimate occupant counts through different approaches and sensors. However, high cost and privacy concerns remain as major barriers, restricting the practice of occupant count detection. In this study, we propose a novel method utilizing data from widely deployed Wi-Fi infrastructure to infer occupant counts through machine learning. Compared with the current indirect measurement methods, our method improves the performance of estimating people count: (1) we avoid privacy concerns by anonymizing and reshuffling the MAC addresses on a daily basis; (2) we adopted a heuristic feature engineer approach to cluster connected devices into different types based on their daily connection duration. We tested the method in an office building located in California. In an area with an average occupancy of 22-27 people and a peak occupancy of 48-74 people, the root square mean error on the test set is less than four people. The error is within two people counts for more than 70% of estimations, and less than six counts for more than 90% of estimations, indicating a relatively high accuracy. The major contribution of this study is proposing a novel and accurate approach to detect occupant counts in a non-intrusive way, i.e., utilizing existing Wi-Fi infrastructure in buildings without requiring the installation of extra hardware or sensors. The method we proposed is generic and could be applied to other commercial buildings to infer occupant counts for energy efficient building control.
机译：减少建筑物能耗的一种重要方法是根据占用率信息优化建筑物控制。提出了各种研究以通过不同的方法和传感器来估计人数。然而，高成本和隐私问题仍然是主要障碍，限制了乘员计数检测的实践。在这项研究中，我们提出了一种新颖的方法，利用广泛部署的Wi-Fi基础设施中的数据通过机器学习来推断人数。与当前的间接测量方法相比，我们的方法提高了估算人数的性能：（1）通过每天匿名和重新安排MAC地址来避免隐私问题； （2）我们采用启发式功能工程师的方法，根据连接设备的每日连接持续时间将其分为不同类型。我们在加利福尼亚的一栋办公楼中测试了该方法。在平均居住人数为22-27人，高峰居住人数为48-74人的区域中，测试集的均方根误差小于4。误差在两个人内，占估计值的70％以上，而在六个人以内，估计值的90％以上，表明准确性相对较高。这项研究的主要贡献在于，提出了一种新颖且准确的方法，以一种非侵入性的方式检测乘员人数，即利用建筑物中现有的Wi-Fi基础设施，而无需安装额外的硬件或传感器。我们提出的方法是通用的，可以应用于其他商业建筑，以推断占用人数以实现节能建筑控制。

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