Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

Detalhes bibliográficos
Autor(a) principal: Ramos, Daniel
Data de Publicação: 2020
Outros Autores: Faria, Pedro, Vale, Zita, Mourinho, João, Correia, Regina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/17285
Resumo: This article belongs to the Special Issue Time Series Forecasting for Energy Consumption
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spelling Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental LearningArtificial neural networksElectricity consumptionMachine learningLoad forecastIndustrial facilityThis article belongs to the Special Issue Time Series Forecasting for Energy ConsumptionSociety’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical resultsThis work has received funding from Portugal 2020 under the SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of the ITEA 3 SPEAR Project 16001, from FEDER Funds through the COMPETE program and from National Funds through FCT under the project UIDB/00760/2020 and CEECIND/02887/2017.MDPIRepositório Científico do Instituto Politécnico do PortoRamos, DanielFaria, PedroVale, ZitaMourinho, JoãoCorreia, Regina2021-03-04T18:08:19Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/17285eng10.3390/en13184774info: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:RCAAP2023-03-13T13:06:39Zoai:recipp.ipp.pt:10400.22/17285Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:48.721481Repositório 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 Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
title Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
spellingShingle Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
Ramos, Daniel
Artificial neural networks
Electricity consumption
Machine learning
Load forecast
Industrial facility
title_short Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
title_full Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
title_fullStr Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
title_full_unstemmed Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
title_sort Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
author Ramos, Daniel
author_facet Ramos, Daniel
Faria, Pedro
Vale, Zita
Mourinho, João
Correia, Regina
author_role author
author2 Faria, Pedro
Vale, Zita
Mourinho, João
Correia, Regina
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Ramos, Daniel
Faria, Pedro
Vale, Zita
Mourinho, João
Correia, Regina
dc.subject.por.fl_str_mv Artificial neural networks
Electricity consumption
Machine learning
Load forecast
Industrial facility
topic Artificial neural networks
Electricity consumption
Machine learning
Load forecast
Industrial facility
description This article belongs to the Special Issue Time Series Forecasting for Energy Consumption
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01T00:00:00Z
2021-03-04T18:08:19Z
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/10400.22/17285
url http://hdl.handle.net/10400.22/17285
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3390/en13184774
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eu_rights_str_mv openAccess
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