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

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 diffi...

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Bibliographic Details
Published in:Scientific reports 2023-12, Vol.13 (1), p.21276-21276, Article 21276
Main Authors: Du, Zehua, Yin, Bo, Zhu, Yuanyuan, Huang, Xianqing, Xu, Jiali
Format: Article
Language:English
Subjects:
639/166
639/4077
Accuracy
Algorithms
Appliances
Classification
Cluster analysis
Decomposition
Efficiency
Electricity
Energy consumption
Humanities and Social Sciences
Identification
Mapping
Methods
multidisciplinary
Neural networks
Optimization
Pattern recognition
Science
Science (multidisciplinary)
Smart grid technology
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-48736-8