Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques

Detalhes bibliográficos
Autor(a) principal: Bot, Karol
Data de Publicação: 2021
Outros Autores: Laouali, Inoussa, Ruano, António, Ruano, Maria da Graça
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103881
https://doi.org/10.3390/en14185852
Resumo: At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.
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spelling Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniqueshome energy management systemsbuilding energymodel-based predictive controlbranch-and-bound algorithmsensitivity analysisphotovoltaicsbatteryAt a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.MDPI2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103881http://hdl.handle.net/10316/103881https://doi.org/10.3390/en14185852eng1996-1073Bot, KarolLaouali, InoussaRuano, AntónioRuano, Maria da Graçainfo: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:RCAAP2022-12-06T21:39:41Zoai:estudogeral.uc.pt:10316/103881Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:38.819602Repositório 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 Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
spellingShingle Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
Bot, Karol
home energy management systems
building energy
model-based predictive control
branch-and-bound algorithm
sensitivity analysis
photovoltaics
battery
title_short Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_full Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_fullStr Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_full_unstemmed Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_sort Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
author Bot, Karol
author_facet Bot, Karol
Laouali, Inoussa
Ruano, António
Ruano, Maria da Graça
author_role author
author2 Laouali, Inoussa
Ruano, António
Ruano, Maria da Graça
author2_role author
author
author
dc.contributor.author.fl_str_mv Bot, Karol
Laouali, Inoussa
Ruano, António
Ruano, Maria da Graça
dc.subject.por.fl_str_mv home energy management systems
building energy
model-based predictive control
branch-and-bound algorithm
sensitivity analysis
photovoltaics
battery
topic home energy management systems
building energy
model-based predictive control
branch-and-bound algorithm
sensitivity analysis
photovoltaics
battery
description At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.
publishDate 2021
dc.date.none.fl_str_mv 2021
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/10316/103881
http://hdl.handle.net/10316/103881
https://doi.org/10.3390/en14185852
url http://hdl.handle.net/10316/103881
https://doi.org/10.3390/en14185852
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1996-1073
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
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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
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