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.本研究为美国纽约州波茨坦市克拉克森大学校园内学生公寓楼的41名志愿学生，在其手机上设计并部署了地理围栏（geo-fencing）应用程序。研究于2018年2月4日至5月10日期间，以分钟级粒度记录了受试者的进出活动，并剔除了学期假期（2月21日至25日、3月16日至25日）内的相关数据。另有5名受试者因数据缺失被排除出本次数据集。研究人员按日期与当日时刻对每位受试者的记录事件进行标准化整理：将"进入"事件标记为1，"离开"事件标记为0，以此计算各时刻的在场概率。在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。本次分析采用两种方法推导工作日与周末的居住时段模式：分别为简单平均法与K均值（K-means）聚类算法。本数据集同时提供了分析所用的原始输入数据集，以及两种分析方法的全部输出结果。所有居住时段记录将按每周单日、工作日及周末分别进行呈现。