@inproceedings{60c72bd7f9d64b9d80f43e10641574f3,
title = "Poster abstract: Long term occupancy estimation in a commercial space: An empirical study",
abstract = "Understanding occupancy paterns in a building is very useful to control HVAC systems for improving energy efficiency of the building and occupant comfort. There has been a very litle atempt to understand long term occupancy paterns in a commercial space. In this work, we leverage depth sensors (Kinect for XBOX One) to collect occupancy count from an 11,000 square foot commercial space (Bosch office) for nine months. We analyze the collected data and describe key findings that provide deep insights about how a commercial space is used and serve as a guideline to formulate novel and efficient control strategies.",
keywords = "HVAC control, Occupancy data collection, Occupancy prediction",
author = "Liu, \{Kin Sum\} and Sirajum Munir and Jonathan Francis and Charles Shelton and Shan Lin",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017 ; Conference date: 18-04-2017 Through 20-04-2017",
year = "2017",
month = apr,
day = "18",
doi = "10.1145/3055031.3055062",
language = "English",
series = "Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "307--308",
booktitle = "Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017",
}