Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/10400.22/9356 |
Resumo: | The electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the ‘Marco Legal Estable’ – Stable Legal Framework –, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented. |
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Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input DataArtificial neural networkElectricity marketsPrice forecastRegression modelVolatilityThe electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the ‘Marco Legal Estable’ – Stable Legal Framework –, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented.ElsevierRepositório Científico do Instituto Politécnico do PortoOrtiz, MaríaUkar, OlatzAzevedo, FilipeMúgica, Arantza2016-052117-01-01T00:00:00Z2016-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/9356eng10.1016/j.ijepes.2015.11.004metadata 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:RCAAP2023-03-13T12:49:57Zoai:recipp.ipp.pt:10400.22/9356Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:29:30.281603Repositório 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 |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
title |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
spellingShingle |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data Ortiz, María Artificial neural network Electricity markets Price forecast Regression model Volatility |
title_short |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
title_full |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
title_fullStr |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
title_full_unstemmed |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
title_sort |
Price Forecasting and Validation in the Spanish Electricity Market using Forecasts as Input Data |
author |
Ortiz, María |
author_facet |
Ortiz, María Ukar, Olatz Azevedo, Filipe Múgica, Arantza |
author_role |
author |
author2 |
Ukar, Olatz Azevedo, Filipe Múgica, Arantza |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Ortiz, María Ukar, Olatz Azevedo, Filipe Múgica, Arantza |
dc.subject.por.fl_str_mv |
Artificial neural network Electricity markets Price forecast Regression model Volatility |
topic |
Artificial neural network Electricity markets Price forecast Regression model Volatility |
description |
The electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the ‘Marco Legal Estable’ – Stable Legal Framework –, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-05 2016-05-01T00:00:00Z 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 |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/9356 |
url |
http://hdl.handle.net/10400.22/9356 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.ijepes.2015.11.004 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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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1799131390920359936 |