Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/22106
Autoria: | Labiadh, M. Obrecht, C. Ferreira da Silva, C. Ghodous, P. |
Data: | 2021 |
Título próprio: | A microservice-based framework for exploring data selection for cross-building knowledge transfer |
Volume: | 15 |
Número: | 2 |
Paginação: | 97 - 107 |
ISSN: | 1863-2386 |
DOI (Digital Object Identifier): | 10.1007/s11761-020-00306-w |
Palavras-chave: | Data selection Domain generalization Knowledge transfer Data-driven modeling Energy consumption modeling |
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. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2020-SOCA-POST-PRINT.pdf | Versão Aceite | 679,74 kB | Adobe PDF | Ver/Abrir |
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