Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images
Non-intrusive load monitoring (NILM) is crucial because it helps monitor the operating status of electrical appliances online; detailed power consumption data regarding the appliances can then be obtained. However, the identification of resistive appliances that have similar features in a power grid...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9437198/ |
| _version_ | 1818609549673758720 |
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| author | Dongning Jia Yunxin Li Zehua Du Jiali Xu Bo Yin |
| author_facet | Dongning Jia Yunxin Li Zehua Du Jiali Xu Bo Yin |
| author_sort | Dongning Jia |
| collection | DOAJ |
| description | Non-intrusive load monitoring (NILM) is crucial because it helps monitor the operating status of electrical appliances online; detailed power consumption data regarding the appliances can then be obtained. However, the identification of resistive appliances that have similar features in a power grid is still a major problem. In this study, the reconstructed image of a voltage–current (VI) trajectory is used as input data for a convolutional neural network (CNN) to classify the appliances, particularly resistive appliances. Two dataset PLAID and IDOUC are introduced to verify the performance of the proposed method. According to the results, the excellent performance of the reconstructed VI image method for the identification of the household appliances with similar waveform is validated by comparing it with the other two methods. |
| first_indexed | 2024-12-16T15:00:19Z |
| format | Article |
| id | doaj.art-9d67a6e9aa084545bdc1338ae1df5661 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T15:00:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9d67a6e9aa084545bdc1338ae1df56612022-12-21T22:27:18ZengIEEEIEEE Access2169-35362021-01-019773497735810.1109/ACCESS.2021.30824329437198Non-Intrusive Load Identification Using Reconstructed Voltage–Current ImagesDongning Jia0Yunxin Li1Zehua Du2https://orcid.org/0000-0003-1116-322XJiali Xu3https://orcid.org/0000-0002-3022-5687Bo Yin4https://orcid.org/0000-0001-6318-0174School of Information Science and Engineering, Ocean University of China, Qingdao, ChinaSchool of Information Science and Engineering, Ocean University of China, Qingdao, ChinaSchool of Information Science and Engineering, Ocean University of China, Qingdao, ChinaPilot National Laboratory for Marine Science and Technology, Qingdao, ChinaSchool of Information Science and Engineering, Ocean University of China, Qingdao, ChinaNon-intrusive load monitoring (NILM) is crucial because it helps monitor the operating status of electrical appliances online; detailed power consumption data regarding the appliances can then be obtained. However, the identification of resistive appliances that have similar features in a power grid is still a major problem. In this study, the reconstructed image of a voltage–current (VI) trajectory is used as input data for a convolutional neural network (CNN) to classify the appliances, particularly resistive appliances. Two dataset PLAID and IDOUC are introduced to verify the performance of the proposed method. According to the results, the excellent performance of the reconstructed VI image method for the identification of the household appliances with similar waveform is validated by comparing it with the other two methods.https://ieeexplore.ieee.org/document/9437198/Energy managementload disaggregationnon-intrusive load monitoringreconstructed VI image |
| spellingShingle | Dongning Jia Yunxin Li Zehua Du Jiali Xu Bo Yin Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images IEEE Access Energy management load disaggregation non-intrusive load monitoring reconstructed VI image |
| title | Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images |
| title_full | Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images |
| title_fullStr | Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images |
| title_full_unstemmed | Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images |
| title_short | Non-Intrusive Load Identification Using Reconstructed Voltage–Current Images |
| title_sort | non intrusive load identification using reconstructed voltage x2013 current images |
| topic | Energy management load disaggregation non-intrusive load monitoring reconstructed VI image |
| url | https://ieeexplore.ieee.org/document/9437198/ |
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