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A NILM load identification method based on structured V-I mapping.
- Source :
- Scientific Reports; 12/2/2023, Vol. 13 Issue 1, p1-10, 10p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONVOLUTIONAL neural networks
FEATURE extraction
IDENTIFICATION
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 173962498
- Full Text :
- https://doi.org/10.1038/s41598-023-48736-8