End-to-end NILM system using high frequency data and neural networks.
Resumen:
Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency meter and neural-network based algorithms. The present article presents a novel way to include high frequency information as input of neural network models by means of multivariate time series that include carefully selected features. Furthermore, it provides a detailed assessment of the generalization error and shows that this class of models generalize well to new instances of seen-in-training appliances. An evaluation database formed of measurements in two Uruguayan homes is collected and discussion on general unsupervised approaches is provided
| 2020 | |
|
NILM ANN Energy disaggregation Signal Processing |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/27046 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522897010360320 |
|---|---|
| author | Marchesoni, Franco |
| author2 | Mariño, Camilo Masquil, Elías Massaferro Saquieres, Pablo Fernández, Alicia |
| author2_role | author author author author |
| author_facet | Marchesoni, Franco Mariño, Camilo Masquil, Elías Massaferro Saquieres, Pablo Fernández, Alicia |
| author_role | author |
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| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Marchesoni Franco, Universidad de la República (Uruguay). Facultad de Ingeniería. Mariño Camilo, Universidad de la República (Uruguay). Facultad de Ingeniería. Masquil Elías, Universidad de la República (Uruguay). Facultad de Ingeniería. Massaferro Saquieres Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería. Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.coverage.spatial.es.fl_str_mv | Uruguay |
| dc.creator.none.fl_str_mv | Marchesoni, Franco Mariño, Camilo Masquil, Elías Massaferro Saquieres, Pablo Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2021-04-12T18:34:38Z |
| dc.date.available.none.fl_str_mv | 2021-04-12T18:34:38Z |
| dc.date.issued.none.fl_str_mv | 2020 |
| dc.description.abstract.none.fl_txt_mv | Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency meter and neural-network based algorithms. The present article presents a novel way to include high frequency information as input of neural network models by means of multivariate time series that include carefully selected features. Furthermore, it provides a detailed assessment of the generalization error and shows that this class of models generalize well to new instances of seen-in-training appliances. An evaluation database formed of measurements in two Uruguayan homes is collected and discussion on general unsupervised approaches is provided |
| dc.format.extent.es.fl_str_mv | 11 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Marchesoni, F., Mariño, C., Masquil, E. y otros. End-to-end NILM system using high frequency data and neural networks. [Preprint]. EN: Electrical Engineering and Systems Science (eess.SP - Signal Processing), 2020, pp 1–11. arXiv:2004.13905. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/27046 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | arXiv |
| dc.relation.ispartof.es.fl_str_mv | Electrical Engineering and Systems Science (eess.SP - Signal Processing), pp. 1--11, apr. 2020, arXiv:2004.13905. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.en.fl_str_mv | NILM ANN Energy disaggregation Signal Processing |
| dc.title.none.fl_str_mv | End-to-end NILM system using high frequency data and neural networks. |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency meter and neural-network based algorithms. The present article presents a novel way to include high frequency information as input of neural network models by means of multivariate time series that include carefully selected features. Furthermore, it provides a detailed assessment of the generalization error and shows that this class of models generalize well to new instances of seen-in-training appliances. An evaluation database formed of measurements in two Uruguayan homes is collected and discussion on general unsupervised approaches is provided |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_417fb36096be0a8f337970dd750fcd91 |
| identifier_str_mv | Marchesoni, F., Mariño, C., Masquil, E. y otros. End-to-end NILM system using high frequency data and neural networks. [Preprint]. EN: Electrical Engineering and Systems Science (eess.SP - Signal Processing), 2020, pp 1–11. arXiv:2004.13905. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/27046 |
| publishDate | 2020 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Marchesoni Franco, Universidad de la República (Uruguay). Facultad de Ingeniería.Mariño Camilo, Universidad de la República (Uruguay). Facultad de Ingeniería.Masquil Elías, Universidad de la República (Uruguay). Facultad de Ingeniería.Massaferro Saquieres Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería.Uruguay2021-04-12T18:34:38Z2021-04-12T18:34:38Z2020Marchesoni, F., Mariño, C., Masquil, E. y otros. End-to-end NILM system using high frequency data and neural networks. [Preprint]. EN: Electrical Engineering and Systems Science (eess.SP - Signal Processing), 2020, pp 1–11. arXiv:2004.13905.https://hdl.handle.net/20.500.12008/27046Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency meter and neural-network based algorithms. The present article presents a novel way to include high frequency information as input of neural network models by means of multivariate time series that include carefully selected features. Furthermore, it provides a detailed assessment of the generalization error and shows that this class of models generalize well to new instances of seen-in-training appliances. An evaluation database formed of measurements in two Uruguayan homes is collected and discussion on general unsupervised approaches is providedSubmitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2021-04-10T04:46:41Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MMMMF20.pdf: 1795982 bytes, checksum: dd09b362ae55274908555446ef1feaf7 (MD5)Rejected by Machado Jimena (jmachado@fing.edu.uy), reason: va jorge on 2021-04-12T16:00:19Z (GMT)Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2021-04-12T16:52:41Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MMMMF20.pdf: 1795982 bytes, checksum: dd09b362ae55274908555446ef1feaf7 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2021-04-12T18:03:17Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MMMMF20.pdf: 1795982 bytes, checksum: dd09b362ae55274908555446ef1feaf7 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@fic.edu.uy) on 2021-04-12T18:34:38Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MMMMF20.pdf: 1795982 bytes, checksum: dd09b362ae55274908555446ef1feaf7 (MD5) Previous issue date: 202011 p.application/pdfenengarXivElectrical Engineering and Systems Science (eess.SP - Signal Processing), pp. 1--11, apr. 2020, arXiv:2004.13905.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. 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| spellingShingle | End-to-end NILM system using high frequency data and neural networks. Marchesoni, Franco NILM ANN Energy disaggregation Signal Processing |
| status_str | submittedVersion |
| title | End-to-end NILM system using high frequency data and neural networks. |
| title_full | End-to-end NILM system using high frequency data and neural networks. |
| title_fullStr | End-to-end NILM system using high frequency data and neural networks. |
| title_full_unstemmed | End-to-end NILM system using high frequency data and neural networks. |
| title_short | End-to-end NILM system using high frequency data and neural networks. |
| title_sort | End-to-end NILM system using high frequency data and neural networks. |
| topic | NILM ANN Energy disaggregation Signal Processing |
| url | https://hdl.handle.net/20.500.12008/27046 |
