Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10400.22/16783 |
Resumo: | This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecastingElectricity consumptionForecastingFuzzy rule based methodsMOGULThis paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologiesThis work has been developed under the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO).Ediciones Universidad de SalamancaRepositório Científico do Instituto Politécnico do PortoJozi, AriaPinto, TiagoPraça, IsabelSilva, FranciscoTeixeira, BrígidaVale, Zita2021-01-28T15:51:15Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/16783eng2255-286310.14201/ADCAIJ2019815564info: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:RCAAP2023-03-13T13:04:20Zoai:recipp.ipp.pt:10400.22/16783Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:25.110935Repositório 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 |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
title |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
spellingShingle |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting Jozi, Aria Electricity consumption Forecasting Fuzzy rule based methods MOGUL |
title_short |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
title_full |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
title_fullStr |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
title_full_unstemmed |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
title_sort |
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting |
author |
Jozi, Aria |
author_facet |
Jozi, Aria Pinto, Tiago Praça, Isabel Silva, Francisco Teixeira, Brígida Vale, Zita |
author_role |
author |
author2 |
Pinto, Tiago Praça, Isabel Silva, Francisco Teixeira, Brígida Vale, Zita |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Jozi, Aria Pinto, Tiago Praça, Isabel Silva, Francisco Teixeira, Brígida Vale, Zita |
dc.subject.por.fl_str_mv |
Electricity consumption Forecasting Fuzzy rule based methods MOGUL |
topic |
Electricity consumption Forecasting Fuzzy rule based methods MOGUL |
description |
This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2021-01-28T15:51:15Z |
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/10400.22/16783 |
url |
http://hdl.handle.net/10400.22/16783 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2255-2863 10.14201/ADCAIJ2019815564 |
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 |
Ediciones Universidad de Salamanca |
publisher.none.fl_str_mv |
Ediciones Universidad de Salamanca |
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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1799131454943264768 |