TR241: Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference
Abstract/Contents
- 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.
Description
Type of resource | text |
---|---|
Date created | November 2020 |
Creators/Contributors
Author | Sonta, Andrew | |
---|---|---|
Author | Jain, Rishee |
Subjects
Subject | Building occupants |
---|---|
Subject | buildings |
Subject | inference algorithms |
Subject | social networks |
Subject | design |
Genre | Technical report |
Bibliographic information
Related Publication | Journal Article Journal or Conference Name: IEEE Embedded Systems Letters |
---|---|
Location | https://purl.stanford.edu/xf347zm5032 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
Preferred citation
- Preferred Citation
Sonta, Andrew and Jain, Rishee. (2020). TR241: Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference
. Stanford Digital Repository. Available at: https://purl.stanford.edu/xf347zm5032
Collection
CIFE Publications
View other items in this collection in SearchWorksContact information
- Contact
- cife-email@stanford.edu
Also listed in
Loading usage metrics...