Contextual learning for energy forecasting in buildings
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10400.22/22113 |
Resumo: | Energy consumers are becoming active players in the power and energy system. However, their informed and real-time responsiveness to the variations of renewable-based generation and, consequently, energy prices, is not possible without decision support solutions. This paper proposes a novel contextual learning approach for energy forecasting, which supports the decisions of Building Energy Management Systems (BEMS). The proposed forecasting approach includes a contextual dimension that identifies different observed contexts and clusters them according to their similarity. The identification of such contexts is used by the learning process of state-of-the-art artificial intelligence-based forecasting methods to select and adapt the most relevant data that is used in the training phase in each context. Forecasts for energy consumption, generation, temperature, brightness and occupancy are used by the BEMS to provide recommendations to the consumers and to support automated control of building devices. Real consumption, generation and contextual data gathered from several sensors in a building are used to validate the results, which show that the proposed contextual learning model improves forecasts of energy consumption, generation and other relevant factors for energy management in buildings. |
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Contextual learning for energy forecasting in buildingsContextual learningDemand responseEnergy consumptionEnergy management systemsForecastingEnergy consumers are becoming active players in the power and energy system. However, their informed and real-time responsiveness to the variations of renewable-based generation and, consequently, energy prices, is not possible without decision support solutions. This paper proposes a novel contextual learning approach for energy forecasting, which supports the decisions of Building Energy Management Systems (BEMS). The proposed forecasting approach includes a contextual dimension that identifies different observed contexts and clusters them according to their similarity. The identification of such contexts is used by the learning process of state-of-the-art artificial intelligence-based forecasting methods to select and adapt the most relevant data that is used in the training phase in each context. Forecasts for energy consumption, generation, temperature, brightness and occupancy are used by the BEMS to provide recommendations to the consumers and to support automated control of building devices. Real consumption, generation and contextual data gathered from several sensors in a building are used to validate the results, which show that the proposed contextual learning model improves forecasts of energy consumption, generation and other relevant factors for energy management in buildings.This work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760/2020.ElsevierRepositório Científico do Instituto Politécnico do PortoJozi, AriaPinto, TiagoVale, Zita2023-02-02T12:24:35Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/22113eng10.1016/j.ijepes.2021.107707metadata only accessinfo: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:RCAAP2023-03-13T13:18:28Zoai:recipp.ipp.pt:10400.22/22113Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:42:10.095868Repositório 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 |
Contextual learning for energy forecasting in buildings |
title |
Contextual learning for energy forecasting in buildings |
spellingShingle |
Contextual learning for energy forecasting in buildings Jozi, Aria Contextual learning Demand response Energy consumption Energy management systems Forecasting |
title_short |
Contextual learning for energy forecasting in buildings |
title_full |
Contextual learning for energy forecasting in buildings |
title_fullStr |
Contextual learning for energy forecasting in buildings |
title_full_unstemmed |
Contextual learning for energy forecasting in buildings |
title_sort |
Contextual learning for energy forecasting in buildings |
author |
Jozi, Aria |
author_facet |
Jozi, Aria Pinto, Tiago Vale, Zita |
author_role |
author |
author2 |
Pinto, Tiago Vale, Zita |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Jozi, Aria Pinto, Tiago Vale, Zita |
dc.subject.por.fl_str_mv |
Contextual learning Demand response Energy consumption Energy management systems Forecasting |
topic |
Contextual learning Demand response Energy consumption Energy management systems Forecasting |
description |
Energy consumers are becoming active players in the power and energy system. However, their informed and real-time responsiveness to the variations of renewable-based generation and, consequently, energy prices, is not possible without decision support solutions. This paper proposes a novel contextual learning approach for energy forecasting, which supports the decisions of Building Energy Management Systems (BEMS). The proposed forecasting approach includes a contextual dimension that identifies different observed contexts and clusters them according to their similarity. The identification of such contexts is used by the learning process of state-of-the-art artificial intelligence-based forecasting methods to select and adapt the most relevant data that is used in the training phase in each context. Forecasts for energy consumption, generation, temperature, brightness and occupancy are used by the BEMS to provide recommendations to the consumers and to support automated control of building devices. Real consumption, generation and contextual data gathered from several sensors in a building are used to validate the results, which show that the proposed contextual learning model improves forecasts of energy consumption, generation and other relevant factors for energy management in buildings. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-02-02T12:24:35Z |
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/10400.22/22113 |
url |
http://hdl.handle.net/10400.22/22113 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.ijepes.2021.107707 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131508001210368 |