In this article an energy disaggregation architecture using elastic matching algorithms is
presented. The architecture uses a database of reference energy consumption signatures …

A data-driven methodology to improve the energy disaggregation accuracy during Non-
Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification …

Sažetak The implementation of non-intrusive load monitoring has gained significant
attention as a promising solution for disaggregating and identifying individual appliances' …

Non-Intrusive Load Monitoring aims to disaggregate the energy consumption measurements
of a smart-meter from household to device level. Most commercial smart-meters measure at …

An extension of the baseline non-intrusive load monitoring approach for energy
disaggregation using temporal contextual information is presented in this paper. In detail …

In this study, we propose a deep learning-based NILM technique using actual measured
power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks …

In the last decade, the significant development of smart building technologies has led to the
formation of various energy sensing and monitoring applications. Energy monitoring of …

This study aims to address the problem of load identification using single-point sensing. The
problem of load identification forms the basis for nonintrusive load monitoring, which is an …

Non-intrusive load monitoring (NILM) is a promising approach to provide energy
consumption monitoring of electrical appliances and analysis of current and voltage data …

With the rapid integration of behind-the-meter (BTM) resources, monitoring the working
status of these resources has become a very important task, followed by distributed terminal …