Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting

Detalhes bibliográficos
Autor(a) principal: Melo, J. D. [UNESP]
Data de Publicação: 2015
Outros Autores: Padilha-Feltrin, A. [UNESP], Carreno, E. M.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ISAP.2015.7325537
http://hdl.handle.net/11449/177925
Resumo: Distribution utilities must perform forecasts in spatial manner to determine the locations that could increase their electric demand. In general, these forecasts are made in the urban area, without regard to the preferences of the inhabitants to develop its activities outside the city boundary. This may lead to errors in decision making of the distribution network expansion planning. In order to identify such preferences, this paper presents a geographically weighted regression that explore spatial patterns to determines the probability of rural regions become urban zones, as part of the urban sprawl. The proposed method is applied in a Brazilian midsize city, showing that the use of the calculated probabilities decreases the global error of spatial load forecasting in 6.5% of the load growth.
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spelling Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecastingDistribution network planninggeographically weighted regressionspatial electric load forecastingspatial regressionDistribution utilities must perform forecasts in spatial manner to determine the locations that could increase their electric demand. In general, these forecasts are made in the urban area, without regard to the preferences of the inhabitants to develop its activities outside the city boundary. This may lead to errors in decision making of the distribution network expansion planning. In order to identify such preferences, this paper presents a geographically weighted regression that explore spatial patterns to determines the probability of rural regions become urban zones, as part of the urban sprawl. The proposed method is applied in a Brazilian midsize city, showing that the use of the calculated probabilities decreases the global error of spatial load forecasting in 6.5% of the load growth.Dept. Electrical Engineering University of the State of Sao Paulo UNESPCenter for Engineering and Mathematical Sciences State University of West Parana UNIOESTEDept. Electrical Engineering University of the State of Sao Paulo UNESPUniversidade Estadual Paulista (Unesp)UNIOESTEMelo, J. D. [UNESP]Padilha-Feltrin, A. [UNESP]Carreno, E. M.2018-12-11T17:27:42Z2018-12-11T17:27:42Z2015-11-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ISAP.2015.73255372015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015.http://hdl.handle.net/11449/17792510.1109/ISAP.2015.73255372-s2.0-84962290825Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015info:eu-repo/semantics/openAccess2024-07-04T19:11:33Zoai:repositorio.unesp.br:11449/177925Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:03:38.512590Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
title Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
spellingShingle Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
Melo, J. D. [UNESP]
Distribution network planning
geographically weighted regression
spatial electric load forecasting
spatial regression
title_short Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
title_full Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
title_fullStr Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
title_full_unstemmed Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
title_sort Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
author Melo, J. D. [UNESP]
author_facet Melo, J. D. [UNESP]
Padilha-Feltrin, A. [UNESP]
Carreno, E. M.
author_role author
author2 Padilha-Feltrin, A. [UNESP]
Carreno, E. M.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
UNIOESTE
dc.contributor.author.fl_str_mv Melo, J. D. [UNESP]
Padilha-Feltrin, A. [UNESP]
Carreno, E. M.
dc.subject.por.fl_str_mv Distribution network planning
geographically weighted regression
spatial electric load forecasting
spatial regression
topic Distribution network planning
geographically weighted regression
spatial electric load forecasting
spatial regression
description Distribution utilities must perform forecasts in spatial manner to determine the locations that could increase their electric demand. In general, these forecasts are made in the urban area, without regard to the preferences of the inhabitants to develop its activities outside the city boundary. This may lead to errors in decision making of the distribution network expansion planning. In order to identify such preferences, this paper presents a geographically weighted regression that explore spatial patterns to determines the probability of rural regions become urban zones, as part of the urban sprawl. The proposed method is applied in a Brazilian midsize city, showing that the use of the calculated probabilities decreases the global error of spatial load forecasting in 6.5% of the load growth.
publishDate 2015
dc.date.none.fl_str_mv 2015-11-10
2018-12-11T17:27:42Z
2018-12-11T17:27:42Z
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/ISAP.2015.7325537
2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015.
http://hdl.handle.net/11449/177925
10.1109/ISAP.2015.7325537
2-s2.0-84962290825
url http://dx.doi.org/10.1109/ISAP.2015.7325537
http://hdl.handle.net/11449/177925
identifier_str_mv 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015.
10.1109/ISAP.2015.7325537
2-s2.0-84962290825
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015
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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