Multi-scale attention mechanism network for Nonintrusive load decomposition Article Swipe
Weidong Tang
,
Hong Zhang
,
Xiaodong Zhang
,
Wei Zhuang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2492/1/012019
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2492/1/012019
Nonintrusive load decomposition is an important prerequisite to realize intelligent power monitoring and a key application of the smart grid. The existing algorithms cannot achieve the decomposition effect with high accuracy and have poor performance in low-frequency loads. To solve these problems, a method based on a multi-scale attention mechanism network is proposed. First, construct the hole residual attention module to extract the deep features and help the network learn the important features of high peak areas and low-frequency electrical appliances in the time series data. Then, the multi-scale fusion module is proposed to fuse the characteristic information after the convolution operation of different scales. Finally, the decomposed active power values of multiple target electrical appliances are output through the full connection layer. The proposed work can decompose the load characteristics of each electrical appliance in the house by the nonintrusive method. The experimental results on the dataset indicate that this work can reduce the decomposition error and has excellent load decomposition capability compared with the existing methods.
Related Topics
Concepts
Fuse (electrical)
Decomposition
Computer science
Residual
Decomposition method (queueing theory)
Convolution (computer science)
Smart grid
Scale (ratio)
Power (physics)
Grid
Electric power system
Real-time computing
Artificial intelligence
Algorithm
Engineering
Artificial neural network
Electrical engineering
Geometry
Biology
Quantum mechanics
Physics
Mathematics
Ecology
Discrete mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2492/1/012019
- https://iopscience.iop.org/article/10.1088/1742-6596/2492/1/012019/pdf
- OA Status
- diamond
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4372292497
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4372292497Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2492/1/012019Digital Object Identifier
- Title
-
Multi-scale attention mechanism network for Nonintrusive load decompositionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-01Full publication date if available
- Authors
-
Weidong Tang, Hong Zhang, Xiaodong Zhang, Wei ZhuangList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2492/1/012019Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2492/1/012019/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2492/1/012019/pdfDirect OA link when available
- Concepts
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Fuse (electrical), Decomposition, Computer science, Residual, Decomposition method (queueing theory), Convolution (computer science), Smart grid, Scale (ratio), Power (physics), Grid, Electric power system, Real-time computing, Artificial intelligence, Algorithm, Engineering, Artificial neural network, Electrical engineering, Geometry, Biology, Quantum mechanics, Physics, Mathematics, Ecology, Discrete mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
6Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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