Forecasting electricity prices

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2020
Outros Autores: Groznik, Aleš, Popovič, Aleš
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/99133
Resumo: Castelli, M., Groznik, A., & Popovič, A. (2020). Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119
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spelling Forecasting electricity pricesA machine learning approachBased programmingElectricity pricesEnergy sectorForecastingGeometric semanticMachine learningTheoretical Computer ScienceNumerical AnalysisComputational Theory and MathematicsComputational MathematicsCastelli, M., Groznik, A., & Popovič, A. (2020). Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique-namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCastelli, MauroGroznik, AlešPopovič, Aleš2020-06-10T00:45:03Z2020-05-082020-05-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/99133eng1999-4893PURE: 18512056https://doi.org/10.3390/A13050119info: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:RCAAP2024-03-11T04:46:11Zoai:run.unl.pt:10362/99133Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:08.049491Repositório 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 Forecasting electricity prices
A machine learning approach
title Forecasting electricity prices
spellingShingle Forecasting electricity prices
Castelli, Mauro
Based programming
Electricity prices
Energy sector
Forecasting
Geometric semantic
Machine learning
Theoretical Computer Science
Numerical Analysis
Computational Theory and Mathematics
Computational Mathematics
title_short Forecasting electricity prices
title_full Forecasting electricity prices
title_fullStr Forecasting electricity prices
title_full_unstemmed Forecasting electricity prices
title_sort Forecasting electricity prices
author Castelli, Mauro
author_facet Castelli, Mauro
Groznik, Aleš
Popovič, Aleš
author_role author
author2 Groznik, Aleš
Popovič, Aleš
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Groznik, Aleš
Popovič, Aleš
dc.subject.por.fl_str_mv Based programming
Electricity prices
Energy sector
Forecasting
Geometric semantic
Machine learning
Theoretical Computer Science
Numerical Analysis
Computational Theory and Mathematics
Computational Mathematics
topic Based programming
Electricity prices
Energy sector
Forecasting
Geometric semantic
Machine learning
Theoretical Computer Science
Numerical Analysis
Computational Theory and Mathematics
Computational Mathematics
description Castelli, M., Groznik, A., & Popovič, A. (2020). Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119
publishDate 2020
dc.date.none.fl_str_mv 2020-06-10T00:45:03Z
2020-05-08
2020-05-08T00:00:00Z
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/10362/99133
url http://hdl.handle.net/10362/99133
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1999-4893
PURE: 18512056
https://doi.org/10.3390/A13050119
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
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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