Query-adaptive training data recommendation for cross-building predictive modeling
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10071/26678 |
Resumo: | Predictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance. |
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Query-adaptive training data recommendation for cross-building predictive modelingTraining data recommendationSimilarity learningDomain generalizationKnowledge transferData-driven modelingBuilding energyPredictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance.Springer2023-10-31T00:00:00Z2023-01-01T00:00:00Z20232023-04-03T12:13:53Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/26678eng0219-137710.1007/s10115-022-01771-9Labiadh, M.Obrecht, C.Ferreira da Silva, C.Ghodous, P.Benabdeslem, K.info: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:RCAAP2023-12-03T01:17:38Zoai:repositorio.iscte-iul.pt:10071/26678Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:12:35.891999Repositório 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 |
Query-adaptive training data recommendation for cross-building predictive modeling |
title |
Query-adaptive training data recommendation for cross-building predictive modeling |
spellingShingle |
Query-adaptive training data recommendation for cross-building predictive modeling Labiadh, M. Training data recommendation Similarity learning Domain generalization Knowledge transfer Data-driven modeling Building energy |
title_short |
Query-adaptive training data recommendation for cross-building predictive modeling |
title_full |
Query-adaptive training data recommendation for cross-building predictive modeling |
title_fullStr |
Query-adaptive training data recommendation for cross-building predictive modeling |
title_full_unstemmed |
Query-adaptive training data recommendation for cross-building predictive modeling |
title_sort |
Query-adaptive training data recommendation for cross-building predictive modeling |
author |
Labiadh, M. |
author_facet |
Labiadh, M. Obrecht, C. Ferreira da Silva, C. Ghodous, P. Benabdeslem, K. |
author_role |
author |
author2 |
Obrecht, C. Ferreira da Silva, C. Ghodous, P. Benabdeslem, K. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Labiadh, M. Obrecht, C. Ferreira da Silva, C. Ghodous, P. Benabdeslem, K. |
dc.subject.por.fl_str_mv |
Training data recommendation Similarity learning Domain generalization Knowledge transfer Data-driven modeling Building energy |
topic |
Training data recommendation Similarity learning Domain generalization Knowledge transfer Data-driven modeling Building energy |
description |
Predictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-31T00:00:00Z 2023-01-01T00:00:00Z 2023 2023-04-03T12:13:53Z |
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/10071/26678 |
url |
http://hdl.handle.net/10071/26678 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0219-1377 10.1007/s10115-022-01771-9 |
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 |
Springer |
publisher.none.fl_str_mv |
Springer |
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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1799134681558417408 |