DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts

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Sensing accurately the number of occupants in the rooms of a building enables
many important applications for smart building operation and energy
management. A range of sensor technologies has been studied and applied to the
problem. However, it is costly to achieve high accuracy by instrumenting all
rooms in a building with dedicated occupant sensors. In this
paper, we propose a new concept for estimating accurate room-level counts of occupants. The idea is to disaggregate accurate building-level counts via
existing common sensors available at the room level. This solution is
cost-effective as it scales to large buildings without requiring dedicated
sensors in each room. We propose an algorithm named DCount that implements this
concept. Our results document that DCount can provide room-level counts with a
low normalized root mean squared error of 0.93. This is a major improvement
compared to a state-of-the-art algorithm using common sensors and ventilation
rate measurements resulting in a normalized root mean squared error of 1.54 on
the same data set. Further more, we demonstrate how the results enable
occupant-driven analysis of plug-load consumption which is one out of many
applications using accurate room-level counts of occupants we hope to enable by proposing DCount.
TitelProceedings of the 19th IEEE International Conference on Mobile Data Management
Publikationsdato13. jul. 2018
ISBN (Trykt)978-1-5386-4134-7
ISBN (Elektronisk)978-1-5386-4133-0
StatusUdgivet - 13. jul. 2018
Begivenhed19th IEEE International Conference on Mobile Data Management - Aalborg, Danmark
Varighed: 25. jun. 201828. jun. 2018


Konference19th IEEE International Conference on Mobile Data Management