Datasets for Occupancy Profiles in Student Housing for Occupant Behavior Studies and Application in Building Energy Simulation

Published: 16-10-2020| Version 1 | DOI: 10.17632/hx5mp695tv.1
Leila Nikdel,
Alan E.S. Schay,
Daqing Hou,
Susan Powers


A geo-fencing app was designed and installed on the cellphones of 41 volunteer students living in student housing buildings on Clarkson University’s campus (Potsdam NY, USA). Occupants’ entering and exiting activities were recorded minutely from February 4 to May 10, 2018, with days in the semester breaks (February 21-25 and March 16-25) excluded. Five participants were excluded due to missing data. Recorded events were sorted out for each individual by the date and time of day considering 1 for ‘entered’ events and 0 for ‘exited’ events to show the probability of presence at each time of day. Accounting for excluded days (234 days with errors and uncertainties), 1,096 daily occupancy schedules were retained in the dataset. Two methods were used to analyze the dataset and derive weekday and weekend occupancy schedules. A simple averaging method and K-means clustering techniques were performed. We provide the input datasets that were used for analysis as well as the outputs of both methods. Occupancy schedules are presented separately for each day of a week, weekdays, and weekend days.