A microservice-based framework for exploring data selection for cross-building knowledge transfer
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/10071/22106 |
Resumo: | Supervised deep learning has achieved remarkable success in various applications. Successful machine learning application however depends on the availability of sufficiently large amount of data. In the absence of data from the target domain, representative data collection from multiple sources is often needed. However, a model trained on existing multi-source data might generalize poorly on the unseen target domain. This problem is referred to as domain shift. In this paper, we explore the suitability of multi-source training data selection to tackle the domain shift challenge in the context of domain generalization. We also propose a microservice-oriented methodology for supporting this solution. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select energy consumption data. |
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A microservice-based framework for exploring data selection for cross-building knowledge transferData selectionDomain generalizationKnowledge transferData-driven modelingEnergy consumption modelingSupervised deep learning has achieved remarkable success in various applications. Successful machine learning application however depends on the availability of sufficiently large amount of data. In the absence of data from the target domain, representative data collection from multiple sources is often needed. However, a model trained on existing multi-source data might generalize poorly on the unseen target domain. This problem is referred to as domain shift. In this paper, we explore the suitability of multi-source training data selection to tackle the domain shift challenge in the context of domain generalization. We also propose a microservice-oriented methodology for supporting this solution. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select energy consumption data.Springer2021-11-11T00:00:00Z2021-01-01T00:00:00Z20212021-05-17T14:07:38Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/22106eng1863-238610.1007/s11761-020-00306-wLabiadh, M.Obrecht, C.Ferreira da Silva, C.Ghodous, P.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:RCAAP2023-11-09T17:55:39Zoai:repositorio.iscte-iul.pt:10071/22106Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:28:24.095855Repositório 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 |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
title |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
spellingShingle |
A microservice-based framework for exploring data selection for cross-building knowledge transfer Labiadh, M. Data selection Domain generalization Knowledge transfer Data-driven modeling Energy consumption modeling |
title_short |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
title_full |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
title_fullStr |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
title_full_unstemmed |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
title_sort |
A microservice-based framework for exploring data selection for cross-building knowledge transfer |
author |
Labiadh, M. |
author_facet |
Labiadh, M. Obrecht, C. Ferreira da Silva, C. Ghodous, P. |
author_role |
author |
author2 |
Obrecht, C. Ferreira da Silva, C. Ghodous, P. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Labiadh, M. Obrecht, C. Ferreira da Silva, C. Ghodous, P. |
dc.subject.por.fl_str_mv |
Data selection Domain generalization Knowledge transfer Data-driven modeling Energy consumption modeling |
topic |
Data selection Domain generalization Knowledge transfer Data-driven modeling Energy consumption modeling |
description |
Supervised deep learning has achieved remarkable success in various applications. Successful machine learning application however depends on the availability of sufficiently large amount of data. In the absence of data from the target domain, representative data collection from multiple sources is often needed. However, a model trained on existing multi-source data might generalize poorly on the unseen target domain. This problem is referred to as domain shift. In this paper, we explore the suitability of multi-source training data selection to tackle the domain shift challenge in the context of domain generalization. We also propose a microservice-oriented methodology for supporting this solution. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select energy consumption data. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-11T00:00:00Z 2021-01-01T00:00:00Z 2021 2021-05-17T14:07:38Z |
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/10071/22106 |
url |
http://hdl.handle.net/10071/22106 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1863-2386 10.1007/s11761-020-00306-w |
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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1799134846247763968 |