Learning from Correlated Events for Equipment Relation Inference in Buildings
Learning from Correlated Events for Equipment Relation Inference in Buildings
复制标题
从相关事件中学习以进行建筑物中的设备关系推理
DOI：
10.1145/3360322.3360852
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Whitehouse, Kamin
中科院分区：
文献类型：
--
作者：
Hong, Dezhi;Cai, Renqin;Wang, Hongning;Whitehouse, Kamin
现代建筑产生成千上万的数据流，自动推断这些数据的物理背景的能力是实现大规模建筑分析的关键。由于获取这些背景信息目前是一个耗时且容易出错的手动过程，因此在本研究中，我们首次尝试自动推断建筑物中设备的一个重要背景方面-每个设备如何在功能上与另一个设备连接。我们的解决方案背后的主要见解是，功能连接的设备暴露于物理世界中的相同事件，从而在两个设备的时间序列数据中产生相关变化。然而，由于事件在时间序列中具有不确定的长度，因此识别事件需要解决一个非多项式组合数据分割问题。我们提出了一种解决方案，首先从感官时间序列数据中提取潜在事件，然后使用定制的相关程序筛选出一致事件，以识别设备之间的关系。我们从位于美国不同地理区域的5座不同规模的商业建筑中收集了1000多件设备，并对我们的方法进行了评估。结果表明，该方法的关联推断准确率为94.38%，而最佳基线的关联推断准确率为85.49%。
Modern buildings produce thousands of data streams, and the ability to automatically infer the physical context of such data is the key to enabling building analytics at scale. As acquiring this contextual information is currently a time-consuming and error-prone manual process, in this study we make the first attempt at automatically inferring one important contextual aspect of the equipment in buildings --- how each equipment is functionally connected with another. The main insight behind our solution is that functionally connected equipment is exposed to the same events in the physical world, creating correlated changes in the time series data of both equipment. Because events are of indeterminate length in time series, however, identifying them requires solving a non-polynomial combinatorial data segmentation problem. We present a solution that first extracts latent events from the sensory time series data, and then sifts out coincident events with a customized correlation procedure to identify the relationship between equipment. We evaluated our approach on data collected from over 1,000 pieces of equipment from 5 commercial buildings of various sizes located in different geographical regions in the US. Results show that this approach achieves 94.38% accuracy in relation inference, compared to 85.49% by the best baseline.
模拟建筑对 HVAC 分区的热响应
DOI：
--
发表时间：
2012
期刊：
SIGBED
影响因子：
--
作者：
V. Smith;Tamim I. Sookoor;K. Whitehouse
通讯作者：
K. Whitehouse
DOI：
10.1109/tpami.1984.4767596
发表时间：
1984-01-01
影响因子：
23.6
作者：
GEMAN, S;GEMAN, D
通讯作者：
GEMAN, D