Event Detection for Energy Consumption Monitoring

被引:2
|
作者
Jorde, Daniel [1 ]
Jacobsen, Hans-Arno [1 ]
机构
[1] Tech Univ Munich, Dept Comp Sci, D-80333 Munich, Germany
来源
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING | 2021年 / 6卷 / 04期
关键词
Event detection; Measurement; Machine learning; Detectors; Energy consumption; Approximation algorithms; Partitioning algorithms; non-intrusive load monitoring; neural nets; machine learning; energy-aware systems; DISAGGREGATION; EFFICIENT;
D O I
10.1109/TSUSC.2020.3012066
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The accurate detection of appliance state transitions in electrical signals is fundamental for numerous energy-conserving applications. We present an extensive overview and categorization of the current state in event detection on high-sampling-rate signals. Existing approaches are designed for specific environments and need to be tediously adapted for new ones. Thus, we propose an unsupervised, multi-environment event detector, outperforming four state-of-the-art algorithms on two heterogeneous public datasets.
引用
收藏
页码:703 / 709
页数:7
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