Inferring building occupant ties
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 work, we develop a statistical methodology for inferring this network, which comprises social, spatial, and organizational ties among occupants. 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.