Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns

Detalhes bibliográficos
Autor(a) principal: Vinagre, Eugénia
Data de Publicação: 2016
Outros Autores: Paz, Juan F. De, Pinto, Tiago, Vale, Zita, Corchado, Juan M., Garcia, Oscar
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/9996
Resumo: The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation – campus of the Polytechnic of Porto, in real time.
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spelling Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patternsArtificial Neural NetworkElectricity ConsumptionSolar RadiationSupport Vector RegressionThe increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation – campus of the Polytechnic of Porto, in real time.Institute of Electrical and Electronics EngineersRepositório Científico do Instituto Politécnico do PortoVinagre, EugéniaPaz, Juan F. DePinto, TiagoVale, ZitaCorchado, Juan M.Garcia, Oscar20162117-01-01T00:00:00Z2016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/9996eng10.1109/SSCI.2016.7849853metadata only accessinfo: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:RCAAP2023-03-13T12:51:34Zoai:recipp.ipp.pt:10400.22/9996Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:30:32.524156Repositório 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 Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
title Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
spellingShingle Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
Vinagre, Eugénia
Artificial Neural Network
Electricity Consumption
Solar Radiation
Support Vector Regression
title_short Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
title_full Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
title_fullStr Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
title_full_unstemmed Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
title_sort Intelligent Energy Forecasting based on the Correlation between Solar Radiation and Consumption patterns
author Vinagre, Eugénia
author_facet Vinagre, Eugénia
Paz, Juan F. De
Pinto, Tiago
Vale, Zita
Corchado, Juan M.
Garcia, Oscar
author_role author
author2 Paz, Juan F. De
Pinto, Tiago
Vale, Zita
Corchado, Juan M.
Garcia, Oscar
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 Vinagre, Eugénia
Paz, Juan F. De
Pinto, Tiago
Vale, Zita
Corchado, Juan M.
Garcia, Oscar
dc.subject.por.fl_str_mv Artificial Neural Network
Electricity Consumption
Solar Radiation
Support Vector Regression
topic Artificial Neural Network
Electricity Consumption
Solar Radiation
Support Vector Regression
description The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation – campus of the Polytechnic of Porto, in real time.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-01-01T00:00:00Z
2117-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/9996
url http://hdl.handle.net/10400.22/9996
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1109/SSCI.2016.7849853
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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
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