In recent decades, many studies have attempted to develop a reliable occupancy model using either rule-based, stochastic, data-driven, or agent-based approaches. These are based on the hypothesis that occupant presence can become predictable provided sufficient knowledge and data are provided. However, a different view propounds that occupant presence could follow a random-walk pattern or become unpredictable in certain types of rooms/buildings, for example, university labs and library buildings. In this study, the authors report the predictability of occupant presence in high-rise residential apartment buildings in South Korea. The authors collected occupant presence data from 31 households over 147 days using occupancy sensors installed in each household. The predictability of occupant presence was then analyzed using the normalized cumulative periodogram (NCP) and Bartlett’s test. It was found that (1) the predictability of occupant presence is significantly influenced by temporal and spatial resolutions, (2) extending measurement periods (e.g., 7 days vs 147 days) can increase the predictability of occupant presence, (3) for a measurement period of 7 days, the occupant presence for 14 households became unpredictable, and (4) the predictability of occupant presence significantly differs among 31 households.

近几十年来，许多研究都试图使用基于规则、随机、数据驱动或基于代理的方法来开发可靠的占用模型。这些是基于这样的假设，即只要提供足够的知识和数据，乘员的存在就可以变得可预测。然而，另一种观点认为，在某些类型的房间/建筑物（例如大学实验室和图书馆大楼）中，居住者的存在可能遵循随机游走模式或变得不可预测。在这项研究中，作者报告了韩国高层住宅公寓楼中居住者的可预测性。作者使用安装在每个家庭中的占用传感器在 147 天内从 31 个家庭收集了居住者存在数据。然后使用归一化累积周期图 (NCP) 和 Bartlett 检验分析乘员存在的可预测性。发现（1）居住者存在的可预测性受到时间和空间分辨率的显着影响，（2）延长测量周期（例如，7 天对 147 天）可以增加居住者存在的可预测性，（3）测量在 7 天的时间里，14 户的住户存在变得不可预测，并且 (4) 31 户的住户存在的可预测性存在显着差异。