Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions

Detalhes bibliográficos
Autor(a) principal: Rodrigues, J. L.
Data de Publicação: 2019
Outros Autores: Morro-Mello, I. [UNESP], Melo, J. D., Padilha-Feltrin, A. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ISGT-LA.2019.8895367
http://hdl.handle.net/11449/199729
Resumo: The acquisition of electric vehicles depends on socioeconomic factors and does not occur homogeneously in the different zones of urban areas for the early years of the electric vehicles penetrations. The concentration of these vehicles can be found by spatial regressions that correlate statically the electric vehicles rate by subarea with the socioeconomic factors of their neighboring regions. Such correlation allows characterizing the influence of the inhabitants in neighboring regions to the purchase of electric vehicles. Therefore, this work aims to show how spatial regressions can provide useful information to determine the load growth by the electric vehicles recharging. To exemplify the information quality, provide from such regression classes, the application of two regressions is performed for a medium-sized city in Brazil in order to determine the best location of charging stations for electric vehicles and the maximum diversified demand in each subarea.
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spelling Estimation of Electric Demand from Electric Vehicles Using Spatial RegressionsCharging stationsElectric vehiclesGeospatial analysisRegression analysisTransportationThe acquisition of electric vehicles depends on socioeconomic factors and does not occur homogeneously in the different zones of urban areas for the early years of the electric vehicles penetrations. The concentration of these vehicles can be found by spatial regressions that correlate statically the electric vehicles rate by subarea with the socioeconomic factors of their neighboring regions. Such correlation allows characterizing the influence of the inhabitants in neighboring regions to the purchase of electric vehicles. Therefore, this work aims to show how spatial regressions can provide useful information to determine the load growth by the electric vehicles recharging. To exemplify the information quality, provide from such regression classes, the application of two regressions is performed for a medium-sized city in Brazil in order to determine the best location of charging stations for electric vehicles and the maximum diversified demand in each subarea.Federal University of ABC-UFABC Engineering Modeling and Applied Social Sciences CenterSão Paulo State University - UNESP FEIS Dept. of Electrical EngineeringSão Paulo State University - UNESP FEIS Dept. of Electrical EngineeringUniversidade Federal do ABC (UFABC)Universidade Estadual Paulista (Unesp)Rodrigues, J. L.Morro-Mello, I. [UNESP]Melo, J. D.Padilha-Feltrin, A. [UNESP]2020-12-12T01:47:46Z2020-12-12T01:47:46Z2019-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ISGT-LA.2019.88953672019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019.http://hdl.handle.net/11449/19972910.1109/ISGT-LA.2019.88953672-s2.0-85075721441Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019info:eu-repo/semantics/openAccess2024-07-04T19:11:19Zoai:repositorio.unesp.br:11449/199729Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:44:21.670173Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
title Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
spellingShingle Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
Rodrigues, J. L.
Charging stations
Electric vehicles
Geospatial analysis
Regression analysis
Transportation
title_short Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
title_full Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
title_fullStr Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
title_full_unstemmed Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
title_sort Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
author Rodrigues, J. L.
author_facet Rodrigues, J. L.
Morro-Mello, I. [UNESP]
Melo, J. D.
Padilha-Feltrin, A. [UNESP]
author_role author
author2 Morro-Mello, I. [UNESP]
Melo, J. D.
Padilha-Feltrin, A. [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Federal do ABC (UFABC)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Rodrigues, J. L.
Morro-Mello, I. [UNESP]
Melo, J. D.
Padilha-Feltrin, A. [UNESP]
dc.subject.por.fl_str_mv Charging stations
Electric vehicles
Geospatial analysis
Regression analysis
Transportation
topic Charging stations
Electric vehicles
Geospatial analysis
Regression analysis
Transportation
description The acquisition of electric vehicles depends on socioeconomic factors and does not occur homogeneously in the different zones of urban areas for the early years of the electric vehicles penetrations. The concentration of these vehicles can be found by spatial regressions that correlate statically the electric vehicles rate by subarea with the socioeconomic factors of their neighboring regions. Such correlation allows characterizing the influence of the inhabitants in neighboring regions to the purchase of electric vehicles. Therefore, this work aims to show how spatial regressions can provide useful information to determine the load growth by the electric vehicles recharging. To exemplify the information quality, provide from such regression classes, the application of two regressions is performed for a medium-sized city in Brazil in order to determine the best location of charging stations for electric vehicles and the maximum diversified demand in each subarea.
publishDate 2019
dc.date.none.fl_str_mv 2019-09-01
2020-12-12T01:47:46Z
2020-12-12T01:47:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ISGT-LA.2019.8895367
2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019.
http://hdl.handle.net/11449/199729
10.1109/ISGT-LA.2019.8895367
2-s2.0-85075721441
url http://dx.doi.org/10.1109/ISGT-LA.2019.8895367
http://hdl.handle.net/11449/199729
identifier_str_mv 2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019.
10.1109/ISGT-LA.2019.8895367
2-s2.0-85075721441
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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