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

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.本研究为居住于美国纽约州波茨坦市克拉克森大学校园学生公寓的41名志愿学生，在其手机上安装了地理围栏（geo-fencing）应用程序。2018年2月4日至5月10日期间，以分钟级精度记录受试者的进出活动，剔除了学期假期（2月21日至25日与3月16日至25日）内的相关数据。因存在数据缺失问题，5名参与者的有效数据被剔除。针对每名受试者的记录事件，按发生日期与当日时刻进行排序，将“进入”事件标记为1，“离开”事件标记为0，以此表征各时刻的在场概率。剔除存在误差与不确定性的234天数据后，本数据集最终保留1096条每日居住时段记录。本研究采用两种方法对数据集进行分析，以提取工作日与周末的居住时段模式：分别为简单平均法与K-means聚类技术。本研究同时提供了分析所用的输入数据集，以及两种分析方法的输出结果。居住时段记录按每周每日、工作日及周末分别单独呈现。