Combining Several Distinct Electrical Features to Enhance Nonintrusive Load Monitoring

被引：0

作者：

Bernard, Timo ^{[1
]}

Klaassen, Julian

Wohland, Daniel

vom Boegel, Gerd ^{[1
]}

机构：

[1] Fraunhofer IMS, TSA, Duisburg, Germany

来源：

2015 INTERNATIONAL CONFERENCE ON SMART GRID AND CLEAN ENERGY TECHNOLOGIES (ICSGCE) | 2015年

关键词：

nonintrusive load monitoring; load disaggregation; smart metering; device identification; electrical features; energy efficiency; energy consumption; load management; unsupervised learning;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Smart meters are state of the art for electricity measurement in domestic and commercial buildings. So far they are only able to track the overall electricity consumption, though appliance specific feedback can lead to substantial higher energy savings. One promising option to reach appliance specific consumption information is nonintrusive load monitoring (NILM), in which this information is gained by disaggregating the overall load profile from a single-point measurement. To improve the accuracy of NILM, in this paper we investigate several distinct electrical features and combine them in an unsupervised learning algorithm. Our algorithm evaluation shows promising results for this method.

引用

页码：139 / 143

页数：5

共 15 条

[1]

[Anonymous], PNNL22635

[2] Is disaggregation the holy grail of energy efficiency? The case of electricity [J].