Strip, Bind, and Search: A method for identifying abnormal energy consumption in buildings
Strip, Bind, and Search: A method for identifying abnormal energy consumption in buildings
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DOI：
10.1145/2461381.2461399
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发表时间：
2013-04
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通讯作者：
Romain Fontugne;Jorge Ortiz;Nicolas Tremblay;P. Borgnat;P. Flandrin;K. Fukuda;D. Culler;H. Esaki
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文献类型：
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作者：
Romain Fontugne;Jorge Ortiz;Nicolas Tremblay;P. Borgnat;P. Flandrin;K. Fukuda;D. Culler;H. Esaki
典型的大型建筑包含数千个传感器，用于监控暖通空调系统、照明和其他操作子系统。随着作业效率的提高，油公司越来越依赖于历史数据处理来发现节能的机会。然而，他们被大量的数据淹没，并寻求更有效的方法来识别潜在的问题。在本文中，我们提出了一种新的方法，称为条带，绑定和搜索（SBS）；一种发现设备异常行为和一致使用模式的方法。SBS揭示设备之间的关系，并为它们相对于其他设备的使用模式构建一个模型。然后标记与模型的偏差。我们在一组建筑传感器轨迹上运行SBS；每个都包含数百个传感器，报告18周内来自两个完全不同基础设施的独立建筑的数据流。我们证明，在许多情况下，SBS揭示了与导致能源浪费的低效设备使用相对应的不当行为。每台设备产生的平均浪费高达2500千瓦时。
A typical large building contains thousands of sensors, monitoring the HVAC system, lighting, and other operational sub-systems. With the increased push for operational efficiency, operators are relying more on historical data processing to uncover opportunities for energy-savings. However, they are overwhelmed with the deluge of data and seek more efficient ways to identify potential problems. In this paper, we present a new approach called the Strip, Bind and Search (SBS); a method for uncovering abnormal equipment behavior and in-concert usage patterns. SBS uncovers relationships between devices and constructs a model for their usage pattern relative to other devices. It then flags deviations from the model. We run SBS on a set of building sensor traces; each containing hundred sensors reporting data flows over 18 weeks from two separate buildings with fundamentally different infrastructures. We demonstrate that, in many cases, SBS uncovers misbehavior corresponding to inefficient device usage that leads to energy waste. The average waste uncovered is as high as 2500 kWh per device.