Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128411640528896 |