Intelligent energy management system for buildings with renewables and vehicle-to-grid charging
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/35771 |
Resumo: | Renewable energies have recently seen a strong development. The awareness of the masses regarding the pollution due to fossil fuels is rising and with it, the use of electric vehicles (EVs). Hence, there is an increasing effort to keep energy distribution sustainable and to find ways of reducing its price. The aim of this study is to build a decision algorithm that will help minimize the electrical bill of a household, making use of V2H (Vehicle-to- Home) chargers. In this approach EVs can be used to store energy, which can then be supplied to the household during periods of high demand. One of the inputs that the designed algorithm requires is the household’s energy consumption forecast. Therefore, a energy consumption predictor was developed in this work altogether with a version that does not require past information of the specific household. This predictor is useful while there is not enough past data to train a more reliable model. The decision algorithm was tested in a simulated environment against a baseline decision algorithm. In the several scenarios and test houses, the proposed approach attained an average of 19.29% decrease in the energy expenses of the household. |
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Intelligent energy management system for buildings with renewables and vehicle-to-grid chargingSmart homeEnergyEVHouseholdLoad demandConsumption forecastDecision algorithmEnergy management systemHEMSMLRenewable energies have recently seen a strong development. The awareness of the masses regarding the pollution due to fossil fuels is rising and with it, the use of electric vehicles (EVs). Hence, there is an increasing effort to keep energy distribution sustainable and to find ways of reducing its price. The aim of this study is to build a decision algorithm that will help minimize the electrical bill of a household, making use of V2H (Vehicle-to- Home) chargers. In this approach EVs can be used to store energy, which can then be supplied to the household during periods of high demand. One of the inputs that the designed algorithm requires is the household’s energy consumption forecast. Therefore, a energy consumption predictor was developed in this work altogether with a version that does not require past information of the specific household. This predictor is useful while there is not enough past data to train a more reliable model. The decision algorithm was tested in a simulated environment against a baseline decision algorithm. In the several scenarios and test houses, the proposed approach attained an average of 19.29% decrease in the energy expenses of the household.Nos últimos anos, as energias renováveis têm sido alvo de um forte desenvolvimento. A conscientização sobre a poluição por combustível fósseis tem vindo a aumentar e, com isso, o uso de veículos elétricos (EVs). Neste sentido, tem havido um esforço para manter a distribuição de energia sustentável e encontrar formas de reduzir o seu preço. O objetivo deste estudo é construir um algoritmo de decisão que ajude a minimizar os custos de energia elétrica de uma residência, fazendo uso de carregadores V2H (Vehicleto- Home). Assim, os EVs podem ser usados como uma forma de armazenar energia que pode ser fornecida de volta à casa durante os períodos de maior necessidade. Uma das informações que o algoritmo proposto requer é a previsão do consumo energético da casa. Portanto, um modelo de previsão de consumo de energia doméstica foi também desenvolvido neste trabalho, incluindo uma versão que não requer informação histórica. Este modelo é útil enquanto não há informação histórica suficiente para treinar um modelo mais confiável. O algoritmo de decisão foi testado num ambiente simulado e comparado com um algoritmo de decisão base. Nos vários cenários e casas testadas, a abordagem proposta obteve uma redução média de 19.29% nas despesas energéticas da casa.2023-01-16T10:18:57Z2022-07-22T00:00:00Z2022-07-22info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/35771engSilva, João Nuno Pereirainfo: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:RCAAP2024-02-22T12:09:09Zoai:ria.ua.pt:10773/35771Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:06:49.075233Repositório 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 |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
title |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
spellingShingle |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging Silva, João Nuno Pereira Smart home Energy EV Household Load demand Consumption forecast Decision algorithm Energy management system HEMS ML |
title_short |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
title_full |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
title_fullStr |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
title_full_unstemmed |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
title_sort |
Intelligent energy management system for buildings with renewables and vehicle-to-grid charging |
author |
Silva, João Nuno Pereira |
author_facet |
Silva, João Nuno Pereira |
author_role |
author |
dc.contributor.author.fl_str_mv |
Silva, João Nuno Pereira |
dc.subject.por.fl_str_mv |
Smart home Energy EV Household Load demand Consumption forecast Decision algorithm Energy management system HEMS ML |
topic |
Smart home Energy EV Household Load demand Consumption forecast Decision algorithm Energy management system HEMS ML |
description |
Renewable energies have recently seen a strong development. The awareness of the masses regarding the pollution due to fossil fuels is rising and with it, the use of electric vehicles (EVs). Hence, there is an increasing effort to keep energy distribution sustainable and to find ways of reducing its price. The aim of this study is to build a decision algorithm that will help minimize the electrical bill of a household, making use of V2H (Vehicle-to- Home) chargers. In this approach EVs can be used to store energy, which can then be supplied to the household during periods of high demand. One of the inputs that the designed algorithm requires is the household’s energy consumption forecast. Therefore, a energy consumption predictor was developed in this work altogether with a version that does not require past information of the specific household. This predictor is useful while there is not enough past data to train a more reliable model. The decision algorithm was tested in a simulated environment against a baseline decision algorithm. In the several scenarios and test houses, the proposed approach attained an average of 19.29% decrease in the energy expenses of the household. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-22T00:00:00Z 2022-07-22 2023-01-16T10:18:57Z |
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/10773/35771 |
url |
http://hdl.handle.net/10773/35771 |
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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1799137723357855744 |