A linear regression pattern for electricity price forecasting in the Iberian electricity market

Detalhes bibliográficos
Autor(a) principal: Ferreira, Ângela P.
Data de Publicação: 2019
Outros Autores: Jenice, Ramos, Fernandes, Paula Odete
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10198/21552
Resumo: The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organised markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.
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spelling A linear regression pattern for electricity price forecasting in the Iberian electricity marketThe Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organised markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.Biblioteca Digital do IPBFerreira, Ângela P.Jenice, RamosFernandes, Paula Odete2020-04-06T11:11:27Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/21552engFerreira, Ângela P.; Jenice, Ramos; Fernandes, Paula O. (2019). A linear regression pattern for electricity price forecasting in the Iberian electricity market. Revista Facultad de Ingeniería Universidad de Antioquia. ISSN 0120-6230. 93, p. 117-1270120-623010.17533/udea.redin.201905222422-2844info: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:RCAAP2023-11-21T10:48:15Zoai:bibliotecadigital.ipb.pt:10198/21552Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:12:37.495797Repositório 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 A linear regression pattern for electricity price forecasting in the Iberian electricity market
title A linear regression pattern for electricity price forecasting in the Iberian electricity market
spellingShingle A linear regression pattern for electricity price forecasting in the Iberian electricity market
Ferreira, Ângela P.
title_short A linear regression pattern for electricity price forecasting in the Iberian electricity market
title_full A linear regression pattern for electricity price forecasting in the Iberian electricity market
title_fullStr A linear regression pattern for electricity price forecasting in the Iberian electricity market
title_full_unstemmed A linear regression pattern for electricity price forecasting in the Iberian electricity market
title_sort A linear regression pattern for electricity price forecasting in the Iberian electricity market
author Ferreira, Ângela P.
author_facet Ferreira, Ângela P.
Jenice, Ramos
Fernandes, Paula Odete
author_role author
author2 Jenice, Ramos
Fernandes, Paula Odete
author2_role author
author
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.contributor.author.fl_str_mv Ferreira, Ângela P.
Jenice, Ramos
Fernandes, Paula Odete
description The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organised markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01T00:00:00Z
2020-04-06T11:11:27Z
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/10198/21552
url http://hdl.handle.net/10198/21552
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ferreira, Ângela P.; Jenice, Ramos; Fernandes, Paula O. (2019). A linear regression pattern for electricity price forecasting in the Iberian electricity market. Revista Facultad de Ingeniería Universidad de Antioquia. ISSN 0120-6230. 93, p. 117-127
0120-6230
10.17533/udea.redin.20190522
2422-2844
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dc.format.none.fl_str_mv application/pdf
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