Two-stage Non-Intrusive Load Monitoring method for multi-state loads.
The loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312954 |
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| _version_ | 1841533142916136960 |
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| author | Lei Wang Xia Han Yushu Cheng Jiaqi Ma Xuerui Zhang Xiaoqing Han |
| author_facet | Lei Wang Xia Han Yushu Cheng Jiaqi Ma Xuerui Zhang Xiaoqing Han |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | The loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address the misidentification of multi-state appliances. The V-I trajectories of loads are at first classified with Resnet18. Then, load features with low redundancy is obtained through the Max-Relevance and Min-Redundancy (mRMR) feature selection algorithm from various operating states of loads that were not successfully classified. The SVM algorithm is developed for two-stage identification to achieve high accuracy of classification for identifying the multi-state appliances quickly. This proposed NILM method can significantly improve the accuracy of identification for multi-state loads. Finally, the Plaid dataset is acquired to validate the effectiveness and accuracy of the proposed method. |
| format | Article |
| id | doaj-art-1c208e67b4b942d3ac933c68c4af5807 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1c208e67b4b942d3ac933c68c4af58072025-01-17T05:31:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031295410.1371/journal.pone.0312954Two-stage Non-Intrusive Load Monitoring method for multi-state loads.Lei WangXia HanYushu ChengJiaqi MaXuerui ZhangXiaoqing HanThe loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address the misidentification of multi-state appliances. The V-I trajectories of loads are at first classified with Resnet18. Then, load features with low redundancy is obtained through the Max-Relevance and Min-Redundancy (mRMR) feature selection algorithm from various operating states of loads that were not successfully classified. The SVM algorithm is developed for two-stage identification to achieve high accuracy of classification for identifying the multi-state appliances quickly. This proposed NILM method can significantly improve the accuracy of identification for multi-state loads. Finally, the Plaid dataset is acquired to validate the effectiveness and accuracy of the proposed method.https://doi.org/10.1371/journal.pone.0312954 |
| spellingShingle | Lei Wang Xia Han Yushu Cheng Jiaqi Ma Xuerui Zhang Xiaoqing Han Two-stage Non-Intrusive Load Monitoring method for multi-state loads. PLoS ONE |
| title | Two-stage Non-Intrusive Load Monitoring method for multi-state loads. |
| title_full | Two-stage Non-Intrusive Load Monitoring method for multi-state loads. |
| title_fullStr | Two-stage Non-Intrusive Load Monitoring method for multi-state loads. |
| title_full_unstemmed | Two-stage Non-Intrusive Load Monitoring method for multi-state loads. |
| title_short | Two-stage Non-Intrusive Load Monitoring method for multi-state loads. |
| title_sort | two stage non intrusive load monitoring method for multi state loads |
| url | https://doi.org/10.1371/journal.pone.0312954 |
| work_keys_str_mv | AT leiwang twostagenonintrusiveloadmonitoringmethodformultistateloads AT xiahan twostagenonintrusiveloadmonitoringmethodformultistateloads AT yushucheng twostagenonintrusiveloadmonitoringmethodformultistateloads AT jiaqima twostagenonintrusiveloadmonitoringmethodformultistateloads AT xueruizhang twostagenonintrusiveloadmonitoringmethodformultistateloads AT xiaoqinghan twostagenonintrusiveloadmonitoringmethodformultistateloads |