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Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference

TitleBuilding Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference
Publication TypeTechnical Report
Year of Publication2020
AuthorsSonta, A, Jain, R
NumberTR241
Date Published11/2020
PublisherCIFE
Place PublishedStanford University
KeywordsBuilding occupants, buildings, Design, inference algorithms, Social Networks
Abstract

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.

URLhttps://purl.stanford.edu/xf347zm5032
Citation Key2556