Machine learning techniques in the energy consumption of buildings

Detalhes bibliográficos
Autor(a) principal: Abdelaziz, Ahmed
Data de Publicação: 2021
Outros Autores: Santos, Vitor, Dias, Miguel Sales
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/128800
Resumo: 109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
id RCAP_c0b5a3d214532b4b7f09215240cb7b32
oai_identifier_str oai:run.unl.pt:10362/128800
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Machine learning techniques in the energy consumption of buildingsA systematic literature review using text mining and bibliometric analysisBibliometric mapEnergy consumption of buildingsIntelligent modelsMachine learningSystematic literature reviewText miningRenewable Energy, Sustainability and the EnvironmentFuel TechnologyEnergy Engineering and Power TechnologyEnergy (miscellaneous)Control and OptimizationElectrical and Electronic EngineeringSDG 7 - Affordable and Clean EnergySDG 11 - Sustainable Cities and CommunitiesSDG 12 - Responsible Consumption and ProductionSDG 13 - Climate Action109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAbdelaziz, AhmedSantos, VitorDias, Miguel Sales2021-12-06T23:43:17Z2021-11-012021-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article31application/pdfhttp://hdl.handle.net/10362/128800eng1996-1073PURE: 35226534https://doi.org/10.3390/en14227810info: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:RCAAP2024-03-11T05:08:11Zoai:run.unl.pt:10362/128800Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:24.087322Repositório 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 Machine learning techniques in the energy consumption of buildings
A systematic literature review using text mining and bibliometric analysis
title Machine learning techniques in the energy consumption of buildings
spellingShingle Machine learning techniques in the energy consumption of buildings
Abdelaziz, Ahmed
Bibliometric map
Energy consumption of buildings
Intelligent models
Machine learning
Systematic literature review
Text mining
Renewable Energy, Sustainability and the Environment
Fuel Technology
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
SDG 13 - Climate Action
title_short Machine learning techniques in the energy consumption of buildings
title_full Machine learning techniques in the energy consumption of buildings
title_fullStr Machine learning techniques in the energy consumption of buildings
title_full_unstemmed Machine learning techniques in the energy consumption of buildings
title_sort Machine learning techniques in the energy consumption of buildings
author Abdelaziz, Ahmed
author_facet Abdelaziz, Ahmed
Santos, Vitor
Dias, Miguel Sales
author_role author
author2 Santos, Vitor
Dias, Miguel Sales
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Abdelaziz, Ahmed
Santos, Vitor
Dias, Miguel Sales
dc.subject.por.fl_str_mv Bibliometric map
Energy consumption of buildings
Intelligent models
Machine learning
Systematic literature review
Text mining
Renewable Energy, Sustainability and the Environment
Fuel Technology
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
SDG 13 - Climate Action
topic Bibliometric map
Energy consumption of buildings
Intelligent models
Machine learning
Systematic literature review
Text mining
Renewable Energy, Sustainability and the Environment
Fuel Technology
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
SDG 13 - Climate Action
description 109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-06T23:43:17Z
2021-11-01
2021-11-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/128800
url http://hdl.handle.net/10362/128800
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1996-1073
PURE: 35226534
https://doi.org/10.3390/en14227810
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 31
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
_version_ 1799138067734331392

Registros relacionados