Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering
Widespread inculcation of smart meter data in modern grid is motivating stakeholders to utilize it for demand response management and achieving energy sustainability goals. One of the methods being used in this regard is Non-Intrusive Load Monitoring (NILM); for disaggregating individual devices fro...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10132857/ |
| _version_ | 1797810635175624704 |
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| author | Muzzamil Ghaffar Shakil Rehman Sheikh Noman Naseer Syed Ali Usama Bashir Salah Soliman Abdul Karim Alkhatib |
| author_facet | Muzzamil Ghaffar Shakil Rehman Sheikh Noman Naseer Syed Ali Usama Bashir Salah Soliman Abdul Karim Alkhatib |
| author_sort | Muzzamil Ghaffar |
| collection | DOAJ |
| description | Widespread inculcation of smart meter data in modern grid is motivating stakeholders to utilize it for demand response management and achieving energy sustainability goals. One of the methods being used in this regard is Non-Intrusive Load Monitoring (NILM); for disaggregating individual devices from a combined load profile. This study combines two spectral clustering strategies using voting-based consensus clustering technique in such a way as to achieve the benefits of both parent strategies. The voters in the consensus are taken to be the solutions proposed by Spectral Cluster-Mean (SC-M) and Spectral Cluster-Eigen Vector (SC-EV) algorithms with different window sizes to achieve diversity. Currently, Spectral Clustering for NILM has been used by few research works and so far, no one technique has achieved higher accuracy in detecting various kinds of devices. The proposed strategy was evaluated on real world data set (REFIT). The results have shown enhanced overall performance by up to 6%. An in-depth analysis of various tuning parameters of SC-M and SC-EV is also presented. These novel contributions increase the feasibility of using spectral clustering and voting based consensus clustering for NILM and may open further avenues of research in this direction. |
| first_indexed | 2024-03-13T07:11:46Z |
| format | Article |
| id | doaj.art-515622ec5a7a49deb8437c25aa8b2ada |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T07:11:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-515622ec5a7a49deb8437c25aa8b2ada2023-06-05T23:00:40ZengIEEEIEEE Access2169-35362023-01-0111531655317510.1109/ACCESS.2023.327948910132857Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus ClusteringMuzzamil Ghaffar0https://orcid.org/0000-0002-2125-7425Shakil Rehman Sheikh1https://orcid.org/0000-0001-8526-0538Noman Naseer2https://orcid.org/0000-0002-2680-6403Syed Ali Usama3Bashir Salah4https://orcid.org/0000-0001-5254-7698Soliman Abdul Karim Alkhatib5https://orcid.org/0000-0002-2942-382XDepartment of Mechatronics and Biomedical Engineering, Air University, Islamabad, PakistanDepartment of Mechatronics and Biomedical Engineering, Air University, Islamabad, PakistanDepartment of Mechatronics and Biomedical Engineering, Air University, Islamabad, PakistanDepartment of Information Engineering and Computer Science, University of Trento, Trento, ItalyDepartment of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Engineering Mathematics and Physics, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, EgyptWidespread inculcation of smart meter data in modern grid is motivating stakeholders to utilize it for demand response management and achieving energy sustainability goals. One of the methods being used in this regard is Non-Intrusive Load Monitoring (NILM); for disaggregating individual devices from a combined load profile. This study combines two spectral clustering strategies using voting-based consensus clustering technique in such a way as to achieve the benefits of both parent strategies. The voters in the consensus are taken to be the solutions proposed by Spectral Cluster-Mean (SC-M) and Spectral Cluster-Eigen Vector (SC-EV) algorithms with different window sizes to achieve diversity. Currently, Spectral Clustering for NILM has been used by few research works and so far, no one technique has achieved higher accuracy in detecting various kinds of devices. The proposed strategy was evaluated on real world data set (REFIT). The results have shown enhanced overall performance by up to 6%. An in-depth analysis of various tuning parameters of SC-M and SC-EV is also presented. These novel contributions increase the feasibility of using spectral clustering and voting based consensus clustering for NILM and may open further avenues of research in this direction.https://ieeexplore.ieee.org/document/10132857/Spectral clusteringvoting based consensus clusteringnon-intrusive load monitoringsmart buildingsenergy disaggregation |
| spellingShingle | Muzzamil Ghaffar Shakil Rehman Sheikh Noman Naseer Syed Ali Usama Bashir Salah Soliman Abdul Karim Alkhatib Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering IEEE Access Spectral clustering voting based consensus clustering non-intrusive load monitoring smart buildings energy disaggregation |
| title | Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering |
| title_full | Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering |
| title_fullStr | Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering |
| title_full_unstemmed | Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering |
| title_short | Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering |
| title_sort | accuracy improvement of non intrusive load monitoring using voting based consensus clustering |
| topic | Spectral clustering voting based consensus clustering non-intrusive load monitoring smart buildings energy disaggregation |
| url | https://ieeexplore.ieee.org/document/10132857/ |
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