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

页码：703 / 709

页数：7

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