Nikdel Leila, Schay Alan E S, Hou Daqing, Powers Susan E
Institute for a Sustainable Environment, 8 Clarkson Ave., Clarkson University, Potsdam NY USA.
Department Electrical and Computer Engineering, 8 Clarkson Ave., Clarkson University, Potsdam NY USA.
Data Brief. 2021 Jun 7;37:107205. doi: 10.1016/j.dib.2021.107205. eCollection 2021 Aug.
Building energy simulation (BES) tools fail to capture diversity among occupants' consumption behaviors by using simple and generic occupancy and load profiles, causing uncertainties in simulation predictions. Thus, generating actual occupancy profiles can lead to more accurate and reliable 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 students living in student housing buildings on Clarkson University'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 errors and uncertainties), 1,096 daily occupancy observations 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 [1]. This article provides 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. To show differences in students' occupancy patterns, occupancy schedules in 7 clusters for weekdays and 3 clusters for weekend days are provided. These datasets can be beneficial for modelers and researchers for either using provided occupancy schedules in BES tools or understanding occupant behaviors in student housing.
建筑能耗模拟（BES）工具通过使用简单且通用的居住情况和负荷曲线，无法捕捉居住者消费行为的多样性，从而导致模拟预测存在不确定性。因此，生成实际的居住情况曲线能够带来更准确、可靠的建筑能耗模拟预测。在本文中，根据高分辨率监测到的居住情况数据，给出了公寓式学生宿舍的居住情况曲线。设计并在克拉克森大学校园学生宿舍楼中41名志愿者学生的手机上安装了一款地理围栏应用程序。记录了2018年2月4日至5月10日期间居住者每分钟的进出活动。根据日期和时间对每个个体记录的事件进行整理，“进入”事件记为1，“离开”事件记为0，以显示每天各时段的在场概率。考虑到排除的天数（234天存在误差和不确定性），数据集中保留了1096条每日居住情况观测数据。使用两种方法分析数据集并得出工作日和周末的居住时间表。执行了简单平均法和K均值聚类技术[1]。本文提供了用于分析的输入数据集以及两种方法的输出结果。按一周中的每一天、工作日和周末分别给出居住时间表。为了显示学生居住模式的差异，提供了工作日7个聚类和周末3个聚类的居住时间表。这些数据集对于建模人员和研究人员而言可能是有益的，他们既可以在建筑能耗模拟工具中使用提供的居住时间表，也可以了解学生宿舍中的居住者行为。
