1. A microservice-based framework for exploring data selection in cross-building knowledge transfer
- Author
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Mouna Labiadh, Christian Obrecht, Parisa Ghodous, Catarina Ferreira da Silva, Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Centre d'Energétique et de Thermique de Lyon (CETHIL), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, and Instituto Universitário de Lisboa (ISCTE-IUL)
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Generalization ,Computer science ,[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] ,Knowledge transfer ,Context (language use) ,data-driven-modeling ,02 engineering and technology ,Machine learning ,computer.software_genre ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Machine Learning (cs.LG) ,Computer Science - Information Retrieval ,Management Information Systems ,Domain (software engineering) ,Data selection ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Computer Science - Databases ,Data-driven modeling ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Selection (linguistics) ,energy consumption modeling ,Engenharia e Tecnologia::Outras Engenharias e Tecnologias [Domínio/Área Científica] ,Domain generalization ,Energy consumption modeling ,[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] ,Data collection ,business.industry ,Deep learning ,Ciências Naturais::Ciências da Computação e da Informação [Domínio/Área Científica] ,Databases (cs.DB) ,020207 software engineering ,Energy consumption ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Hardware and Architecture ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,Artificial intelligence ,business ,computer ,Information Retrieval (cs.IR) ,Software ,Information Systems - Abstract
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., Service Oriented Computing and Applications, Springer, 2020
- Published
- 2020
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