Data-driven modeling of smart builiding energy management

Detalhes bibliográficos
Autor(a) principal: Salama, Raghda Ahmed Abdelkerim
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132389
Resumo: Buildings consume approximately 40% of energy in total, which contributes negatively to the environment. Building Energy Management Systems(BEMS) have been used to monitor energy consumption and increase usage efficiency. In this study, the components and importance of BEMS are emphasized. The data from the management systemoftheChamchuri5building in Chula long korn University, Thailand, were used as a template for data-driven modeling for energy usage in smart buildings to analyze the patterns of energy consumption. Using multilevel modeling on theChamchuri5 building ,the main factors that consume energy on a macro and micro level are analyzed .Energy variation between zones and floors was spotted.
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spelling Data-driven modeling of smart builiding energy managementMachine learningBusiness and data analyticsEnergy efficiencyEnergy managementSmart buildingsDomínio/Área Científica::Ciências Sociais::Economia e GestãoBuildings consume approximately 40% of energy in total, which contributes negatively to the environment. Building Energy Management Systems(BEMS) have been used to monitor energy consumption and increase usage efficiency. In this study, the components and importance of BEMS are emphasized. The data from the management systemoftheChamchuri5building in Chula long korn University, Thailand, were used as a template for data-driven modeling for energy usage in smart buildings to analyze the patterns of energy consumption. Using multilevel modeling on theChamchuri5 building ,the main factors that consume energy on a macro and micro level are analyzed .Energy variation between zones and floors was spotted.Han, QiweiRUNSalama, Raghda Ahmed Abdelkerim2022-02-07T11:33:52Z2021-06-292021-05-202021-06-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/132389TID:202769496enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:11:00Zoai:run.unl.pt:10362/132389Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:26.027423Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Data-driven modeling of smart builiding energy management
title Data-driven modeling of smart builiding energy management
spellingShingle Data-driven modeling of smart builiding energy management
Salama, Raghda Ahmed Abdelkerim
Machine learning
Business and data analytics
Energy efficiency
Energy management
Smart buildings
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Data-driven modeling of smart builiding energy management
title_full Data-driven modeling of smart builiding energy management
title_fullStr Data-driven modeling of smart builiding energy management
title_full_unstemmed Data-driven modeling of smart builiding energy management
title_sort Data-driven modeling of smart builiding energy management
author Salama, Raghda Ahmed Abdelkerim
author_facet Salama, Raghda Ahmed Abdelkerim
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
RUN
dc.contributor.author.fl_str_mv Salama, Raghda Ahmed Abdelkerim
dc.subject.por.fl_str_mv Machine learning
Business and data analytics
Energy efficiency
Energy management
Smart buildings
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Machine learning
Business and data analytics
Energy efficiency
Energy management
Smart buildings
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Buildings consume approximately 40% of energy in total, which contributes negatively to the environment. Building Energy Management Systems(BEMS) have been used to monitor energy consumption and increase usage efficiency. In this study, the components and importance of BEMS are emphasized. The data from the management systemoftheChamchuri5building in Chula long korn University, Thailand, were used as a template for data-driven modeling for energy usage in smart buildings to analyze the patterns of energy consumption. Using multilevel modeling on theChamchuri5 building ,the main factors that consume energy on a macro and micro level are analyzed .Energy variation between zones and floors was spotted.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-29
2021-05-20
2021-06-29T00:00:00Z
2022-02-07T11:33:52Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/132389
TID:202769496
url http://hdl.handle.net/10362/132389
identifier_str_mv TID:202769496
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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