Description

This paper presents an analysis of occupancy and occupancy-related data gathered from an academic office building. The data set contains records from the WiFi access points, motion detectors, CO2 sensors, light power and plug-load meters, and camera-based image processing sensors. Concurrent ground-truth occupant counts were collected on five days. Two sensor fusion model formalisms were developed to blend the information in individual data streams: multiple linear regression and artificial neural networks (ANNs). The results indicate that low-cost data streams that are not intended for occupancy sensing, such as WiFi traffic, CO2 concentration, and light power and plug-load data, perform at least as accurately as motion detectors and camera-based image processing sensors in estimating the total number of building occupants.

Citation: 2019 Annual Conference, Kansas City, MO, Technical Papers

Product Details

Published:

2019

Number of Pages:

18

Units of Measure:

Dual

File Size:

1 file , 8.3 MB

Product Code(s):

D-KC-19-006