Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation

Detalhes bibliográficos
Autor(a) principal: Nascimento, Joao
Data de Publicação: 2019
Outros Autores: Pinto, Tiago, Vale, Zita
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/18484
Resumo: Electricity markets are complex environments with very dynamic characteristics. The large-scale penetration of renewable energy sources has brought an increased uncertainty to generation, which is consequently, reflected in electricity market prices. In this way, novel advanced forecasting methods that are able to predict electricity market prices taking into account the new variables that influence prices variation are required. This paper proposes a new model for day-ahead electricity market prices forecasting based on the application of an artificial neural network. The main novelty of this paper relates to the pre-processing phase, in which the relevant data referring to the different variables that have a direct influence on market prices such as generation, temperature, consumption, among others, is analysed. The association between these variables is performed using spearman correlation, from which results the identification of which data has a larger influence on the market prices variation. This pre-analysis is then used to adapt the training process of the artificial neural network, leading to improved forecasting results, by using the most relevant data in an appropriate way.
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spelling Day-ahead electricity market price forecasting using artificial neural network with spearman data correlationArtificial neural networksDay-ahead spot marketElectricity priceForecastingSpearman correlationElectricity markets are complex environments with very dynamic characteristics. The large-scale penetration of renewable energy sources has brought an increased uncertainty to generation, which is consequently, reflected in electricity market prices. In this way, novel advanced forecasting methods that are able to predict electricity market prices taking into account the new variables that influence prices variation are required. This paper proposes a new model for day-ahead electricity market prices forecasting based on the application of an artificial neural network. The main novelty of this paper relates to the pre-processing phase, in which the relevant data referring to the different variables that have a direct influence on market prices such as generation, temperature, consumption, among others, is analysed. The association between these variables is performed using spearman correlation, from which results the identification of which data has a larger influence on the market prices variation. This pre-analysis is then used to adapt the training process of the artificial neural network, leading to improved forecasting results, by using the most relevant data in an appropriate way.This work has been developed under the MAS-SOCIETY project - PTDC/EEI-EEE/28954/2017 and received funding from UID/EEA/00760/2019, funded by FEDER Funds through COMPETE and by National Funds through FCT.IEEERepositório Científico do Instituto Politécnico do PortoNascimento, JoaoPinto, TiagoVale, Zita2021-09-22T14:42:31Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/18484eng978-1-5386-4722-610.1109/PTC.2019.8810618info: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:RCAAP2023-03-13T13:04:27Zoai:recipp.ipp.pt:10400.22/18484Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:26.314780Repositório 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 Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
title Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
spellingShingle Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
Nascimento, Joao
Artificial neural networks
Day-ahead spot market
Electricity price
Forecasting
Spearman correlation
title_short Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
title_full Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
title_fullStr Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
title_full_unstemmed Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
title_sort Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
author Nascimento, Joao
author_facet Nascimento, Joao
Pinto, Tiago
Vale, Zita
author_role author
author2 Pinto, Tiago
Vale, Zita
author2_role author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Nascimento, Joao
Pinto, Tiago
Vale, Zita
dc.subject.por.fl_str_mv Artificial neural networks
Day-ahead spot market
Electricity price
Forecasting
Spearman correlation
topic Artificial neural networks
Day-ahead spot market
Electricity price
Forecasting
Spearman correlation
description Electricity markets are complex environments with very dynamic characteristics. The large-scale penetration of renewable energy sources has brought an increased uncertainty to generation, which is consequently, reflected in electricity market prices. In this way, novel advanced forecasting methods that are able to predict electricity market prices taking into account the new variables that influence prices variation are required. This paper proposes a new model for day-ahead electricity market prices forecasting based on the application of an artificial neural network. The main novelty of this paper relates to the pre-processing phase, in which the relevant data referring to the different variables that have a direct influence on market prices such as generation, temperature, consumption, among others, is analysed. The association between these variables is performed using spearman correlation, from which results the identification of which data has a larger influence on the market prices variation. This pre-analysis is then used to adapt the training process of the artificial neural network, leading to improved forecasting results, by using the most relevant data in an appropriate way.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01T00:00:00Z
2021-09-22T14:42:31Z
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/10400.22/18484
url http://hdl.handle.net/10400.22/18484
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-1-5386-4722-6
10.1109/PTC.2019.8810618
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 IEEE
publisher.none.fl_str_mv IEEE
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
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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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