Abstract Energy-related occupant behavior is a major source of uncertainty in building and urban energy performance simulations. Standardized assumptions, published by ASHRAE and others in the form of occupancy schedules, are widely used in research and practice, especially on the district-scale. In this work, we gathered location-based services data to create context-specific, data-driven occupancy schedules. Using a web mapping service, we collected data for retail and restaurant uses in the downtown neighborhoods of 13 different U.S. cities to create data-driven schedules for each context. The schedules were compared to ASHRAE standard assumptions using the earth mover's distance approach and the schedules' energy-related features. We found that standard schedules seem to significantly overestimate weekly building occupancy, although the shapes of the schedules are generally similar. The use of standard schedules could therefore, have significant impacts on district-scale energy demand simulations, as the overestimation will be cumulative. As compared to the differences between data-driven and standard schedules, the differences between different locations are significantly smaller. However in extreme cases, the weekly cumulative occupancy and the number of occupied hours differ by more than 30% between locations, which means that context-specific differences together with climatic differences might also impact building performance simulation results. Furthermore, we found differences in daily data between the different days of the week. In particular, the observed behavior on Fridays is significantly different from other weekdays for both considered use-types. This indicates that the conventional categorization of occupant behavior models into three day-types: weekday, Saturday, and Sunday, should be reconsidered.

中文翻译：

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使用基于位置的服务数据进行特定环境的城市占用建模

摘要 与能源相关的居住者行为是建筑和城市能源性能模拟中不确定性的主要来源。由 ASHRAE 和其他机构以入住时间表的形式发布的标准化假设被广泛用于研究和实践，尤其是在地区范围内。在这项工作中，我们收集了基于位置的服务数据，以创建特定于上下文的、数据驱动的入住时间表。使用网络地图服务，我们收集了美国 13 个不同城市市中心街区零售和餐厅使用的数据，以针对每种情况创建数据驱动的时间表。使用推土机的距离方法和时间表的能源相关特征，将时间表与 ASHRAE 标准假设进行了比较。我们发现标准时间表似乎大大高估了每周的建筑入住率，尽管时间表的形状大体相似。因此，标准时间表的使用可能会对区域规模的能源需求模拟产生重大影响，因为高估将是累积的。与数据驱动和标准时间表之间的差异相比，不同地点之间的差异要小得多。然而，在极端情况下，每周累计入住率和占用小时数在不同地点之间相差超过 30%，这意味着特定环境的差异和气候差异也可能影响建筑性能模拟结果。此外，我们发现一周中不同天数之间的每日数据存在差异。特别是，对于两种考虑的使用类型，周五观察到的行为与其他工作日明显不同。