Electrical energy consumption as a function of urban variables
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/231435 |
Resumo: | This paper analyzes the electrical energy consumption of households as a function of urban variables, by modelling the urban thermal environment with Artificial Neural Networks (ANN). The study area was a residential neighbourhood. Urban features of reference points were determined by the following characteristics: urban heat island, sky view factor, and users' income level. For each of these reference points, urban air temperatures at the pedestrian level were collected with data-loggers. At the same time, rural temperatures made available by the city meteorological station site were registered. In addition, the user's profiles were identified by means of a questionnaire applied to the households. Their electrical energy consumption data were also collected from the power supply company. Models applying Artificial Neural Networks were then developed for the most important periods of UHI intensity. The results show that low values of sky view factor and high urban heat islands, when observed in high income zones, are associated with the largest electrical energy consumption patterns. |
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Repositório Institucional da UNESP |
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spelling |
Electrical energy consumption as a function of urban variablesArtificial neural networksEnergy consumptionSky view factorsThis paper analyzes the electrical energy consumption of households as a function of urban variables, by modelling the urban thermal environment with Artificial Neural Networks (ANN). The study area was a residential neighbourhood. Urban features of reference points were determined by the following characteristics: urban heat island, sky view factor, and users' income level. For each of these reference points, urban air temperatures at the pedestrian level were collected with data-loggers. At the same time, rural temperatures made available by the city meteorological station site were registered. In addition, the user's profiles were identified by means of a questionnaire applied to the households. Their electrical energy consumption data were also collected from the power supply company. Models applying Artificial Neural Networks were then developed for the most important periods of UHI intensity. The results show that low values of sky view factor and high urban heat islands, when observed in high income zones, are associated with the largest electrical energy consumption patterns.Department of Architecture, Urbanism and Landscape, State University of São Paulo, BauruUniversidade de São Paulo (USP)De Souza, Léa Cristina LucasDe Oliveira, Alinne Prado2022-04-29T08:45:25Z2022-04-29T08:45:25Z2008-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectPLEA 2008 - Towards Zero Energy Building: 25th PLEA International Conference on Passive and Low Energy Architecture, Conference Proceedings.http://hdl.handle.net/11449/2314352-s2.0-85067774613Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLEA 2008 - Towards Zero Energy Building: 25th PLEA International Conference on Passive and Low Energy Architecture, Conference Proceedingsinfo:eu-repo/semantics/openAccess2024-04-16T19:24:28Zoai:repositorio.unesp.br:11449/231435Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-16T19:24:28Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Electrical energy consumption as a function of urban variables |
title |
Electrical energy consumption as a function of urban variables |
spellingShingle |
Electrical energy consumption as a function of urban variables De Souza, Léa Cristina Lucas Artificial neural networks Energy consumption Sky view factors |
title_short |
Electrical energy consumption as a function of urban variables |
title_full |
Electrical energy consumption as a function of urban variables |
title_fullStr |
Electrical energy consumption as a function of urban variables |
title_full_unstemmed |
Electrical energy consumption as a function of urban variables |
title_sort |
Electrical energy consumption as a function of urban variables |
author |
De Souza, Léa Cristina Lucas |
author_facet |
De Souza, Léa Cristina Lucas De Oliveira, Alinne Prado |
author_role |
author |
author2 |
De Oliveira, Alinne Prado |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
De Souza, Léa Cristina Lucas De Oliveira, Alinne Prado |
dc.subject.por.fl_str_mv |
Artificial neural networks Energy consumption Sky view factors |
topic |
Artificial neural networks Energy consumption Sky view factors |
description |
This paper analyzes the electrical energy consumption of households as a function of urban variables, by modelling the urban thermal environment with Artificial Neural Networks (ANN). The study area was a residential neighbourhood. Urban features of reference points were determined by the following characteristics: urban heat island, sky view factor, and users' income level. For each of these reference points, urban air temperatures at the pedestrian level were collected with data-loggers. At the same time, rural temperatures made available by the city meteorological station site were registered. In addition, the user's profiles were identified by means of a questionnaire applied to the households. Their electrical energy consumption data were also collected from the power supply company. Models applying Artificial Neural Networks were then developed for the most important periods of UHI intensity. The results show that low values of sky view factor and high urban heat islands, when observed in high income zones, are associated with the largest electrical energy consumption patterns. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 2022-04-29T08:45:25Z 2022-04-29T08:45:25Z |
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 |
PLEA 2008 - Towards Zero Energy Building: 25th PLEA International Conference on Passive and Low Energy Architecture, Conference Proceedings. http://hdl.handle.net/11449/231435 2-s2.0-85067774613 |
identifier_str_mv |
PLEA 2008 - Towards Zero Energy Building: 25th PLEA International Conference on Passive and Low Energy Architecture, Conference Proceedings. 2-s2.0-85067774613 |
url |
http://hdl.handle.net/11449/231435 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLEA 2008 - Towards Zero Energy Building: 25th PLEA International Conference on Passive and Low Energy Architecture, Conference Proceedings |
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_ |
1799964734531829760 |