Datasets for occupancy profiles in apartment-style student housing for occupant behavior studies and application in building energy simulation

Building energy simulation (BES) tools fail to capture diver-sity among occupants' consumption behaviors by using sim-ple and generic occupancy and load profiles, causing un-certainties in simulation predictions. Thus, generating ac-tual occupancy profiles can lead to more accurate and re-liable BES predictions. In this article, occupancy profiles for apartment-style student housing are presented from high -resolution monitored occupancy data. A geo-fencing app was designed and installed on the cellphones of 41 volunteer stu-dents living in student housing buildings on Clarkson Univer-sity's campus. Occupants' entering and exiting activities were recorded every minute from February 4 to May 10, 2018. 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 er -rors and uncertainties), 1,096 daily occupancy observations were retained in the dataset. Two methods were used to ana-lyze the dataset and derive weekday and weekend occupancy schedules. A simple averaging method and K-means cluster -ing techniques were performed [1] . This article provides the input datasets that were used for analysis as well as the out-puts of both methods. Occupancy schedules are presented separately for each day of a week, weekdays, and weekend days. To show differences in students' occupancy patterns, occupancy schedules in 7 clusters for weekdays and 3 clus-ters for weekend days are provided. These datasets can be beneficial for modelers and researchers for either using pro-vided occupancy schedules in BES tools or understanding oc-cupant behaviors in student housing. (C) 2021 The Authors. Published by Elsevier Inc.