Design of ensemble forecasting models for home energy management systems
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/17385 |
Resumo: | The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models. |
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Design of ensemble forecasting models for home energy management systemsEnergy systemsMachine learningForecastingEnergy management systemsMulti-objective genetic algorithmsEnsemble modelsEnergy in buildingsThe increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.MDPISapientiaBot, KarolSantos, SamiraHabou Laouali, InoussaRuano, AntonioRuano, Maria da Graça2021-12-13T16:11:28Z2021-11-162021-11-25T16:00:13Z2021-11-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/17385engEnergies 14 (22): 7664 (2021)10.3390/en14227664info: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:RCAAP2023-07-24T10:29:29Zoai:sapientia.ualg.pt:10400.1/17385Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:07:20.907955Repositório 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 |
Design of ensemble forecasting models for home energy management systems |
title |
Design of ensemble forecasting models for home energy management systems |
spellingShingle |
Design of ensemble forecasting models for home energy management systems Bot, Karol Energy systems Machine learning Forecasting Energy management systems Multi-objective genetic algorithms Ensemble models Energy in buildings |
title_short |
Design of ensemble forecasting models for home energy management systems |
title_full |
Design of ensemble forecasting models for home energy management systems |
title_fullStr |
Design of ensemble forecasting models for home energy management systems |
title_full_unstemmed |
Design of ensemble forecasting models for home energy management systems |
title_sort |
Design of ensemble forecasting models for home energy management systems |
author |
Bot, Karol |
author_facet |
Bot, Karol Santos, Samira Habou Laouali, Inoussa Ruano, Antonio Ruano, Maria da Graça |
author_role |
author |
author2 |
Santos, Samira Habou Laouali, Inoussa Ruano, Antonio Ruano, Maria da Graça |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Bot, Karol Santos, Samira Habou Laouali, Inoussa Ruano, Antonio Ruano, Maria da Graça |
dc.subject.por.fl_str_mv |
Energy systems Machine learning Forecasting Energy management systems Multi-objective genetic algorithms Ensemble models Energy in buildings |
topic |
Energy systems Machine learning Forecasting Energy management systems Multi-objective genetic algorithms Ensemble models Energy in buildings |
description |
The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-13T16:11:28Z 2021-11-16 2021-11-25T16:00:13Z 2021-11-16T00:00:00Z |
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/10400.1/17385 |
url |
http://hdl.handle.net/10400.1/17385 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Energies 14 (22): 7664 (2021) 10.3390/en14227664 |
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.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 |
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 |
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1799133317722800128 |