Non-Intrusive Load Monitoring: A Power Consumption Based Relaxation

被引：0
|
作者：
Anderson, Kyle D. [1 ,2 ]
Moura, Jose M. F. [1 ,2 ]
Berges, Mario [1 ,3 ]
机构：
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Elect & Comp Engn Dept, Pittsburgh, PA 15213 USA
[3] Carnegie Mellon Univ, Civil & Environm Engn Dept, Pittsburgh, PA 15213 USA
来源：
2015 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2015年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Obtaining per-device energy consumption estimates in Non-Intrusive Load Monitoring (NILM) has proven to be a challenging task. We present Power Consumption Clustered Non-Intrusive Load Monitoring (PCC-NILM), a relaxation of the NILM problem that estimates the energy consumed by devices operating in different power ranges. The Approximate Power Trace Decomposition Algorithm (APTDA) is presented as an unsupervised, data-driven solution to the PCC-NILM problem. We show that reliable energy estimates can be obtained by crowdsourcing the results from using 1,456 event detectors applied to the publicly available BLUED dataset.
引用
收藏
页码：215 / 219
页数：5
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