Many energy performance analysis methodologies assign buildings a descriptive label that represents their main activity, often known as the primary space usage (PSU). This attribute comes from the intent of the design team based on assumptions of how the majority of the spaces in the building will be used. In reality, the way a building’s occupants use the spaces can be different than what was intended. With the recent growth of hourly electricity meter data from the built environment, there is the opportunity to create unsupervised methods to analyze electricity consumption behavior to understand whether the PSU assigned is accurate. Misclassification or oversimplification of the use of the building is possible using these labels when applied to simulation inputs or benchmarking processes. To work towards accurate characterization of a building’s utilization, we propose a modular methodology for identifying potentially mislabeled buildings using distance-based clustering analysis based on hourly electricity consumption data. This method seeks to segment buildings according to their daily behavior and predict which ones are misfits according to their assigned PSU label. This process finds potentially uncharacteristic behavior that could be an indication of mixed-use or a misclassified PSU. Our results on two public data sets, from the Building Data Genome (BDG) Project and Washington DC (DGS), with 507 and 322 buildings respectively, show that 26% and 33% of these buildings are potentially mislabelled based on their load shape behavior. Such information provides a more realistic insight into their true consumption characteristics, enabling more accurate simulation scenarios. Applications of this process and a discussion of limitations and reproducibility are included.

许多能源绩效分析方法为建筑物分配了代表其主要活动的描述性标签，通常称为主要空间使用量（PSU）。此属性来自设计团队的意图，该假设基于建筑物中大部分空间将如何使用的假设。实际上，建筑物的占用者使用空间的方式可能与预期的不同。随着最近来自建筑环境的每小时电表数据的增长，有机会创建无监督方法来分析用电行为，以了解分配的PSU是否准确。当将这些标签应用于模拟输入或基准测试过程时，可能会错误使用建筑物或对建筑物进行过度简化。为了准确表征建筑物的利用率，我们提出了一种模块化方法，可使用基于小时的用电量数据的基于距离的聚类分析来识别可能贴错标签的建筑物。该方法旨在根据建筑物的日常行为对其进行细分，并预测哪些建筑物是根据为其分配的PSU标签不匹配。此过程发现潜在的不典型行为，可能表示混合使用或PSU分类错误。我们对来自建筑数据基因组（BDG​​）项目和华盛顿特区（DGS）的两个公共数据集分别具有507座和322座建筑物的结果表明，这些建筑物中有26％和33％可能会因其荷载形状行为而被误贴标签。此类信息可以更真实地了解其真实消耗特性，从而实现更准确的仿真方案。包括该过程的应用以及对限制和可重复性的讨论。