On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets

被引：2

作者：

Ahajjam, Mohamed Aymane ^{[1
,2
]}

Essayeh, Chaimaa ^{[1
]}

Ghogho, Mounir ^{[1
,3
]}

Kobbane, Abdellatif ^{[2
]}

机构：

[1] Int Univ Rabat, TICLab, Rabat, Morocco

[2] Mohammed V Univ Rabat, ENSIAS, Rabat, Morocco

[3] Univ Leeds, Sch EEE, Leeds, W Yorkshire, England

来源：

2021 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC 2021) | 2021年

关键词：

NILM; Load identification; Energy disaggregation; multi-label classification; machine learning;

D O I：

10.1109/I2MTC50364.2021.9460059

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises' aggregate consumption. In this work, we target supervised multi-label classification for non-intrusive load identification. We describe how we have created a new dataset from Moroccan households using a low sampling frequency. Then, we analyze the performance of three machine learning models for NILM, and investigate the impact of signal input length on performance.

引用

页数：6

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