Open Access
Open access
Scientific Reports, volume 13, issue 1, publication number 21276

A NILM load identification method based on structured V-I mapping

Zehua Du 1
Bo Yin 1, 2
Yuanyuan Zhu 1
Xianqing Huang 2
Jiali Xu 2
2
 
Pilot National Laboratory for Marine Science and Technology (QingDao), Qingdao, China
Publication typeJournal Article
Publication date2023-12-02
scimago Q1
wos Q1
SJR0.900
CiteScore7.5
Impact factor3.8
ISSN20452322
Multidisciplinary
Abstract

With the increasing number and types of global power loads and the development and popularization of smart grid technology, a large number of researches on load-level non-intrusive load monitoring technology have emerged. However, the unique power characteristics of the load make NILM face the difficult problem of low robustness of feature extraction and low accuracy of classification and identification in the recognition stage. This paper proposes a structured V-I mapping method to address the inherent limitations of traditional V-I trajectory mapping methods from a new perspective. In addition, for the verification of the V-I trajectory mapping method proposed in this paper, the complexity of load characteristics is comprehensively considered, and a lightweight convolutional neural network is designed based on AlexNet. The experimental results on the NILM dataset show that the proposed method significantly improves recognition accuracy compared to existing VI trajectory mapping methods.

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