Understanding the underlying structure of building occupant dynamics is crucial to improving the effectiveness and energy efficiency of commercial buildings, as occupants fundamentally drive building design and operation. In current practice, we largely account for occupant behavior in the design and management of buildings through rudimentary schedules of presence or absence. However, the increasing availability of embedded sensors—such as plug load sensors—offers an opportunity not only to monitor occupants’ activity patterns but also to use these patterns to gain insight into the network structure of occupants. In this letter, we present a statistical methodology for inferring this network, which comprises social, spatial, and organizational ties among occupants. We apply our method to a 7-person office environment in Northern California, and we compare the inferred networks to ground truth social, spatial, and organizational networks obtained through validated survey questions. We demonstrate that this approach offers insights into the complex nature of occupant dynamics, which can ultimately serve as inputs into building design strategies that minimize energy consumption and improve occupant well-being.

中文翻译：

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建立关系：使用嵌入式插头负载传感器进行乘员网络推理

了解建筑居住者动态的基本结构对于提高商业建筑的有效性和能源效率至关重要，因为居住者从根本上推动了建筑设计和运营。在当前的实践中，我们主要通过基本的存在或不存在时间表来考虑建筑物设计和管理中的居住者行为。然而，嵌入式传感器（例如插头负载传感器）的可用性不断提高，不仅可以监控住户的活动模式，还可以使用这些模式深入了解住户的网络结构。在这封信中，我们提出了一种推断这个网络的统计方法，其中包括居住者之间的社会、空间和组织联系。我们将我们的方法应用于北加州的 7 人办公环境，我们将推断的网络与通过验证的调查问题获得的真实社会、空间和组织网络进行比较。我们证明，这种方法提供了对居住者动态复杂性的洞察，最终可以作为建筑设计策略的输入，以最大限度地减少能源消耗并改善居住者的幸福感。