|Title||Accessing Wi-Fi Data for Occupancy Sensing|
|Year of Publication||2017|
|Authors||Marco Pritoni, Mary Ann Piette, Bruce Nordman|
A key issue for saving energy in buildings is to assure that delivery of energy services matches building occupancy as closely as possible, to assure that energy is not wasted providing the services to empty rooms or buildings, and to assure that needed services are provided during all times when desired. Accomplishing this to date has been significantly hampered by a lack of inexpensive mechanisms to obtain and use building-wide and more granular data about occupancy. In recent years, the concept of inferential (or implicit) sensing has been proposed and explored to use data from IT systems that already exists in most buildings, to obtain data that are not perfect, but are nearly free to obtain.
Past work by LBNL and others has primarily demonstrated the principle and potential for this, with a primary focus on data from Wi-Fi networks as the best near-term opportunity for inferential sensing from IT networks. This is due to its near-ubiquity, ease of explanation to many audiences, relative uniformness in deployment, low latency of detection, clear ways to mitigate privacy and security, and other benefits (Price et al., 2015). While the potential and value are clear, researchers or building managers who want to obtain the data lack a source of information to understand what data might exist, what devices may have it, and what mechanisms are available to obtain the data. The purpose of this report is to fill that gap.
The report begins with a review of institutional issues in collecting such data, including very real concerns about privacy and IT security. It then describes the various system architectures used in Wi-Fi systems in commercial buildings today, and a variety of mechanisms that can be used to obtain information from them, including examples of how to use them, and sample output. Next is a summary of how to use such data for several different purposes, from retrospective analysis to dynamic building operation, and a conclusion. An appendix provides additional detail on specific mechanisms available from major equipment manufacturers.
The mechanisms specified in this report can be used by building owners and operators to confirm proper operation and uncover any issues or unexpected conditions as a resource for building owners and researchers interesting in utilizing inferential sensing data.
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