NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives
Resumen:
Due to its impact on household energy use and the adoption of renewable energies, the intelligent management of the power consumption of electric vehicles (EVs) is of great relevance. In the context of widespread clean energy adoption and growing environmental concerns, generating incentives through discounted rates for intelligent residential EV power consumption requires algorithms capable of measuring loads in a disaggregated manner. The deployment of smart meter networks offers the possibility of applying machine learning techniques to estimate EV residential consumption. This work presents an efficient algorithm for the Non Intrusive Load Monitoring (NILM) of EV consumption, which is an adaptation of a method previously proposed for high-powered water heaters. Its performance is compared with methods based on deep neural networks. Results from an actual power demand dataset are discussed, and a comparative analysis is carried out against billing rules based on time slots and historical power consumption data.
| 2023 | |
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Beca Maestría CAP Camilo Mariño Proyecto bajo financiación convenio UTE |
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NILM Electric vehicles Load disaggregation Deep learning Renewable energy sources Power demand Machine learning algorithms Neural networks Water heating Electric vehicles |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://ieee-isgt.org/
https://ieeexplore.ieee.org/document/10066441 https://hdl.handle.net/20.500.12008/36564 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522899708346368 |
|---|---|
| author | Mariño, Camilo |
| author2 | Cossio, Guillermo Massaferro Saquieres, Pablo Di Martino, Matías Gómez, Alvaro Fernández, Alicia |
| author2_role | author author author author author |
| author_facet | Mariño, Camilo Cossio, Guillermo Massaferro Saquieres, Pablo Di Martino, Matías Gómez, Alvaro Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Mariño Camilo, Universidad de la República (Uruguay). Facultad de Ingeniería. Cossio Guillermo, Universidad de la República (Uruguay). Facultad de Ingeniería. Massaferro Saquieres Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería. Di Martino Matías, Duke University Gómez Alvaro, 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 Costa Rica |
| dc.creator.none.fl_str_mv | Mariño, Camilo Cossio, Guillermo Massaferro Saquieres, Pablo Di Martino, Matías Gómez, Alvaro Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2023-03-29T11:59:23Z |
| dc.date.available.none.fl_str_mv | 2023-03-29T11:59:23Z |
| dc.date.issued.none.fl_str_mv | 2023 |
| dc.description.abstract.none.fl_txt_mv | Due to its impact on household energy use and the adoption of renewable energies, the intelligent management of the power consumption of electric vehicles (EVs) is of great relevance. In the context of widespread clean energy adoption and growing environmental concerns, generating incentives through discounted rates for intelligent residential EV power consumption requires algorithms capable of measuring loads in a disaggregated manner. The deployment of smart meter networks offers the possibility of applying machine learning techniques to estimate EV residential consumption. This work presents an efficient algorithm for the Non Intrusive Load Monitoring (NILM) of EV consumption, which is an adaptation of a method previously proposed for high-powered water heaters. Its performance is compared with methods based on deep neural networks. Results from an actual power demand dataset are discussed, and a comparative analysis is carried out against billing rules based on time slots and historical power consumption data. |
| dc.description.sponsorship.none.fl_txt_mv | Beca Maestría CAP Camilo Mariño Proyecto bajo financiación convenio UTE |
| dc.format.extent.es.fl_str_mv | 5 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Mariño, C, Cossio, G, Massaferro Saquieres, P. y otros. NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives [en línea]. EN: 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan 2023, pp 1-5. DOI: 10.1109/ISGT51731.2023.10066441 |
| dc.identifier.doi.none.fl_str_mv | 10.1109/ISGT51731.2023.10066441 |
| dc.identifier.uri.none.fl_str_mv | https://ieee-isgt.org/ https://ieeexplore.ieee.org/document/10066441 https://hdl.handle.net/20.500.12008/36564 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.relation.ispartof.es.fl_str_mv | 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan, pp 1-5 |
| 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.es.fl_str_mv | NILM Electric vehicles Load disaggregation Deep learning Renewable energy sources Power demand Machine learning algorithms Neural networks Water heating Electric vehicles |
| dc.title.none.fl_str_mv | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Due to its impact on household energy use and the adoption of renewable energies, the intelligent management of the power consumption of electric vehicles (EVs) is of great relevance. In the context of widespread clean energy adoption and growing environmental concerns, generating incentives through discounted rates for intelligent residential EV power consumption requires algorithms capable of measuring loads in a disaggregated manner. The deployment of smart meter networks offers the possibility of applying machine learning techniques to estimate EV residential consumption. This work presents an efficient algorithm for the Non Intrusive Load Monitoring (NILM) of EV consumption, which is an adaptation of a method previously proposed for high-powered water heaters. Its performance is compared with methods based on deep neural networks. Results from an actual power demand dataset are discussed, and a comparative analysis is carried out against billing rules based on time slots and historical power consumption data. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_79c82d7b95dd3dd5fd6bfaceec6a566f |
| identifier_str_mv | Mariño, C, Cossio, G, Massaferro Saquieres, P. y otros. NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives [en línea]. EN: 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan 2023, pp 1-5. DOI: 10.1109/ISGT51731.2023.10066441 10.1109/ISGT51731.2023.10066441 |
| 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:20.500.12008/36564 |
| publishDate | 2023 |
| 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 | Mariño Camilo, Universidad de la República (Uruguay). Facultad de Ingeniería.Cossio Guillermo, Universidad de la República (Uruguay). Facultad de Ingeniería.Massaferro Saquieres Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.Di Martino Matías, Duke UniversityGómez Alvaro, Universidad de la República (Uruguay). Facultad de Ingeniería.Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería.UruguayCosta Rica2023-03-29T11:59:23Z2023-03-29T11:59:23Z2023Mariño, C, Cossio, G, Massaferro Saquieres, P. y otros. NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives [en línea]. EN: 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan 2023, pp 1-5. DOI: 10.1109/ISGT51731.2023.10066441https://ieee-isgt.org/https://ieeexplore.ieee.org/document/10066441https://hdl.handle.net/20.500.12008/3656410.1109/ISGT51731.2023.10066441Due to its impact on household energy use and the adoption of renewable energies, the intelligent management of the power consumption of electric vehicles (EVs) is of great relevance. In the context of widespread clean energy adoption and growing environmental concerns, generating incentives through discounted rates for intelligent residential EV power consumption requires algorithms capable of measuring loads in a disaggregated manner. The deployment of smart meter networks offers the possibility of applying machine learning techniques to estimate EV residential consumption. This work presents an efficient algorithm for the Non Intrusive Load Monitoring (NILM) of EV consumption, which is an adaptation of a method previously proposed for high-powered water heaters. Its performance is compared with methods based on deep neural networks. Results from an actual power demand dataset are discussed, and a comparative analysis is carried out against billing rules based on time slots and historical power consumption data.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2023-03-25T19:37:32Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MCMDGF23.pdf: 322754 bytes, checksum: d3d457fda7ccda058e90ffe3551b72b3 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2023-03-28T20:39:13Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MCMDGF23.pdf: 322754 bytes, checksum: d3d457fda7ccda058e90ffe3551b72b3 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-03-29T11:59:23Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) MCMDGF23.pdf: 322754 bytes, checksum: d3d457fda7ccda058e90ffe3551b72b3 (MD5) Previous issue date: 2023Beca Maestría CAP Camilo MariñoProyecto bajo financiación convenio UTE5 p.application/pdfenengIEEE2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan, pp 1-5Las 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. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)NILMElectric vehiclesLoad disaggregationDeep learningRenewable energy sourcesPower demandMachine learning algorithmsNeural networksWater heatingElectric vehiclesNILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentivesPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaMariño, CamiloCossio, GuillermoMassaferro Saquieres, PabloDi Martino, MatíasGómez, AlvaroFernández, AliciaProcesamiento de SeñalesTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/36564/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives Mariño, Camilo NILM Electric vehicles Load disaggregation Deep learning Renewable energy sources Power demand Machine learning algorithms Neural networks Water heating Electric vehicles |
| status_str | publishedVersion |
| title | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| title_full | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| title_fullStr | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| title_full_unstemmed | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| title_short | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| title_sort | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
| topic | NILM Electric vehicles Load disaggregation Deep learning Renewable energy sources Power demand Machine learning algorithms Neural networks Water heating Electric vehicles |
| url | https://ieee-isgt.org/ https://ieeexplore.ieee.org/document/10066441 https://hdl.handle.net/20.500.12008/36564 |
