Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems
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/10400.1/19943 |
Resumo: | This work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques. |
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spelling |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management SystemsMulti-objective genetic algorithmsForecasting modelsEnsemble modelsPrediction intervalsRobust modelsProbabilistic forecastingHome energy management systemsThis work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques.Grant number 72581/2020MDPISapientiaRuano, AntonioRuano, Maria2023-09-04T16:34:35Z2023-07-252023-08-25T12:37:13Z2023-07-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/19943engInventions 8 (4): 96 (2023)10.3390/inventions80400962411-5134info: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:RCAAP2023-09-06T02:00:36Zoai:sapientia.ualg.pt:10400.1/19943Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:28:18.840088Repositório 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 |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
title |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
spellingShingle |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems Ruano, Antonio Multi-objective genetic algorithms Forecasting models Ensemble models Prediction intervals Robust models Probabilistic forecasting Home energy management systems |
title_short |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
title_full |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
title_fullStr |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
title_full_unstemmed |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
title_sort |
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objective Formulation: An application to Home Energy Management Systems |
author |
Ruano, Antonio |
author_facet |
Ruano, Antonio Ruano, Maria |
author_role |
author |
author2 |
Ruano, Maria |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Ruano, Antonio Ruano, Maria |
dc.subject.por.fl_str_mv |
Multi-objective genetic algorithms Forecasting models Ensemble models Prediction intervals Robust models Probabilistic forecasting Home energy management systems |
topic |
Multi-objective genetic algorithms Forecasting models Ensemble models Prediction intervals Robust models Probabilistic forecasting Home energy management systems |
description |
This work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-04T16:34:35Z 2023-07-25 2023-08-25T12:37:13Z 2023-07-25T00:00:00Z |
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/10400.1/19943 |
url |
http://hdl.handle.net/10400.1/19943 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Inventions 8 (4): 96 (2023) 10.3390/inventions8040096 2411-5134 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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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1799133550756233216 |