Identifying Metering Hierarchies with Distance Correlation and Dominance Constraints
Identifying Metering Hierarchies with Distance Correlation and Dominance Constraints
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DOI：
10.1109/icmla55696.2022.00242
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Tak-Shing T. Chan;A. Gibberd
中科院分区：
文献类型：
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作者：
Tak-Shing T. Chan;A. Gibberd
在本文中，我们考虑了一系列完全或部分聚合的智能电表的观察结果，我们的目标是估计计量层次。我们建议通过一种新的Chow-Liu树学习过程来估计这些重要的元数据。我们的方法考虑了一组很容易从消费数据中得出的优势条件的先验知识。除了更传统的基于相关性的方法外，我们还介绍了一种基于距离相关的边缘检测方法。综合实验表明，距离相关和优势条件在恢复树状结构方面具有一定的优势。最后，给出了该方法在某图书馆建筑中的实际应用。
In this paper, we consider observations from a series of smart meters that are either completely or partially aggregated, and our aim is to estimate the metering hierarchy. We propose to estimate this important metadata through a novel adaptation of the Chow–Liu tree learning procedure. Our approach takes into account prior knowledge from a set of dominance conditions that are easily elicited from the consumption data. In addition to more traditional correlation-based approaches we also introduce a distance-correlation-based method for detecting edges. Synthetic experiments show the benefits of distance correlation and the dominance conditions in recovering tree structure. The paper concludes with a real-world application of the method to infer energy metering hierarchies in a library building.