Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
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/10400.22/18484 |
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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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 instacron:RCAAP |
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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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1799131455782125568 |