DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Pesquisa operacional (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000300575 |
Resumo: | ABSTRACT Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities. |
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DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDYdata miningelectricity consumptionload curvesclusteringpatternstime granularityABSTRACT Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.Sociedade Brasileira de Pesquisa Operacional2016-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000300575Pesquisa Operacional v.36 n.3 2016reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2016.036.03.0575info:eu-repo/semantics/openAccessServidone,GabrielaConti,Danteeng2017-02-14T00:00:00Zoai:scielo:S0101-74382016000300575Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2017-02-14T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false |
dc.title.none.fl_str_mv |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
title |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
spellingShingle |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY Servidone,Gabriela data mining electricity consumption load curves clustering patterns time granularity |
title_short |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
title_full |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
title_fullStr |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
title_full_unstemmed |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
title_sort |
DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY |
author |
Servidone,Gabriela |
author_facet |
Servidone,Gabriela Conti,Dante |
author_role |
author |
author2 |
Conti,Dante |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Servidone,Gabriela Conti,Dante |
dc.subject.por.fl_str_mv |
data mining electricity consumption load curves clustering patterns time granularity |
topic |
data mining electricity consumption load curves clustering patterns time granularity |
description |
ABSTRACT Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000300575 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000300575 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0101-7438.2016.036.03.0575 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
dc.source.none.fl_str_mv |
Pesquisa Operacional v.36 n.3 2016 reponame:Pesquisa operacional (Online) instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO |
instname_str |
Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
instacron_str |
SOBRAPO |
institution |
SOBRAPO |
reponame_str |
Pesquisa operacional (Online) |
collection |
Pesquisa operacional (Online) |
repository.name.fl_str_mv |
Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
repository.mail.fl_str_mv |
||sobrapo@sobrapo.org.br |
_version_ |
1750318018137686016 |