An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals
<p dir="ltr">Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a...
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| مؤلفون آخرون: | , , |
| منشور في: |
2020
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| الموضوعات: | |
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| _version_ | 1864513565256318976 |
|---|---|
| author | Yassine Himeur (14147787) |
| author2 | Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author2_role | author author author |
| author_facet | Yassine Himeur (14147787) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yassine Himeur (14147787) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| dc.date.none.fl_str_mv | 2020-09-21T21:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/int.22292 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_intelligent_nonintrusive_load_monitoring_scheme_based_on_2D_phase_encoding_of_power_signals/22258198 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Human-centred computing Artificial Intelligence Human-Computer Interaction Theoretical Computer Science Software |
| dc.title.none.fl_str_mv | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK-DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D-PEP descriptor.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Intelligent Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/int.22292" target="_blank">http://dx.doi.org/10.1002/int.22292</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e76bb8036a8333d702541060caf8ac05 |
| identifier_str_mv | 10.1002/int.22292 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22258198 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signalsYassine Himeur (14147787)Abdullah Alsalemi (6951986)Faycal Bensaali (12427401)Abbes Amira (6952001)Information and computing sciencesArtificial intelligenceHuman-centred computingArtificial IntelligenceHuman-Computer InteractionTheoretical Computer ScienceSoftware<p dir="ltr">Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK-DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D-PEP descriptor.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Intelligent Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/int.22292" target="_blank">http://dx.doi.org/10.1002/int.22292</a></p>2020-09-21T21:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/int.22292https://figshare.com/articles/journal_contribution/An_intelligent_nonintrusive_load_monitoring_scheme_based_on_2D_phase_encoding_of_power_signals/22258198CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/222581982020-09-21T21:00:00Z |
| spellingShingle | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals Yassine Himeur (14147787) Information and computing sciences Artificial intelligence Human-centred computing Artificial Intelligence Human-Computer Interaction Theoretical Computer Science Software |
| status_str | publishedVersion |
| title | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| title_full | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| title_fullStr | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| title_full_unstemmed | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| title_short | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| title_sort | An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals |
| topic | Information and computing sciences Artificial intelligence Human-centred computing Artificial Intelligence Human-Computer Interaction Theoretical Computer Science Software |