Non-Intrusive Load Disaggregation Based on Residual Gated Network Article Swipe
Hui Cao
,
Liguo Weng
,
Min Xia
,
Dezheng Zhang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1757-899x/677/3/032092
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1757-899x/677/3/032092
Non-intrusive load disaggregation is designed to estimate the power consumption of each appliance based on the total power of the appliance in the household. Conventional machine learning algorithms cannot accurately extract semantic information from time series data, which motivates us to implement nonintrusive load disaggregation using residual gated recurrent neural networks model (Res-GRU). First, the networks model use multi-scale convolution kernels networks model extract time series data features, and will get multiple map fusions. Secondly, the networks model use residual learning to deepen the network to extract deep load features. Finally, the networks model use the gated recurrent unit to reset and update high level features. In this way, we can get the output power value of the target appliance. The experimental results show that the proposed network model has a good disaggregation effect.
Related Topics
Concepts
Residual
Reset (finance)
Computer science
Convolution (computer science)
Power (physics)
Artificial intelligence
Series (stratigraphy)
Scale (ratio)
Network model
Artificial neural network
Real-time computing
Data mining
Algorithm
Physics
Biology
Financial economics
Quantum mechanics
Economics
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1757-899x/677/3/032092
- OA Status
- diamond
- Cited By
- 2
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2995711280
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2995711280Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1757-899x/677/3/032092Digital Object Identifier
- Title
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Non-Intrusive Load Disaggregation Based on Residual Gated NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-12-01Full publication date if available
- Authors
-
Hui Cao, Liguo Weng, Min Xia, Dezheng ZhangList of authors in order
- Landing page
-
https://doi.org/10.1088/1757-899x/677/3/032092Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1757-899x/677/3/032092Direct OA link when available
- Concepts
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Residual, Reset (finance), Computer science, Convolution (computer science), Power (physics), Artificial intelligence, Series (stratigraphy), Scale (ratio), Network model, Artificial neural network, Real-time computing, Data mining, Algorithm, Physics, Biology, Financial economics, Quantum mechanics, Economics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2Per-year citation counts (last 5 years)
- References (count)
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12Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 93 |
| corresponding_author_ids | https://openalex.org/A5045380530 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I200845125 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.75694169 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |