Faults in heating, ventilation, and air-conditioning control networks substantially affect energy and comfort performance in commercial buildings. As these control networks are comprised of many sensors and actuators, it is challenging to identify, often subtle, anomalies caused by these faults. In this paper, we develop a cluster analysis method for anomaly detection. The proposed method consolidates the building automation system data into a small number of distinct patterns of operation. These distinct patterns help energy managers discover and interpret anomalies through visualization of these patterns. The method was demonstrated with a year’s worth of building automation system data from 247 thermal zones and an air handling unit. Anomalies associated with zone temperature and airflow control were identified in about one-third of these zones. At the air handling unit-level, we identified anomalies related with three different faults: the use of economizer mode with perimeter heating, and leaky outdoor and return air dampers. The use of economizer mode with perimeter heating affected 39% to 52% of the total operation period and caused the outdoor air damper to remain fully open and the heat recovery unit to remain off during most of the heating season.

供暖，通风和空调控制网络中的故障会严重影响商业建筑的能源和舒适性能。由于这些控制网络由许多传感器和执行器组成，因此很难识别出通常由这些故障引起的异常现象。在本文中，我们开发了一种用于异常检测的聚类分析方法。所提出的方法将建筑物自动化系统数据合并为少量不同的操作模式。这些独特的模式可帮助能源管理者通过可视化这些模式来发现并解释异常情况。247个热区和一个空气处理单元的一年的楼宇自动化系统数据证明了该方法。在这些区域的大约三分之一中发现了与区域温度和气流控制相关的异常。在空气处理单元级别，我们确定了与三种不同故障相关的异常情况：使用带周界加热装置的省煤器模式以及泄漏的室外和回风阀。在整体供暖期间使用省煤器模式会影响总运行时间的39％至52％，并导致室外空气挡板在整个供暖季节的大部分时间内保持完全打开，而热量回收装置保持关闭状态。