Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market
The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very im...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/133920 |
| Acceso en línea: | https://hdl.handle.net/11441/133920 https://doi.org/10.3390/app11136097 |
| Access Level: | acceso abierto |
| Palabra clave: | Electricity price forecasting Deep learning Day-ahead market Time series forecasting |
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Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity MarketVega Márquez, BelénRubio Escudero, CristinaNepomuceno Chamorro, Isabel de los ÁngelesArcos Vargas, ÁngelElectricity price forecastingDeep learningDay-ahead marketTime series forecastingThe importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the modelMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-2778MDPILenguajes y Sistemas InformáticosOrganización Industrial y Gestión de Empresas ITIC134: Sistemas InformáticosMinisterio de Ciencia, Innovación y Universidades (MICINN). EspañaJunta de Andalucía2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/133920https://doi.org/10.3390/app11136097reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésApplied Sciences, 11 (13)TIN2017-88209-C2US-1263341P18-RT-2778https://www.mdpi.com/2076-3417/11/13/6097info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1339202026-06-17T12:51:07Z |
| dc.title.none.fl_str_mv |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| title |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| spellingShingle |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market Vega Márquez, Belén Electricity price forecasting Deep learning Day-ahead market Time series forecasting |
| title_short |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| title_full |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| title_fullStr |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| title_full_unstemmed |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| title_sort |
Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market |
| dc.creator.none.fl_str_mv |
Vega Márquez, Belén Rubio Escudero, Cristina Nepomuceno Chamorro, Isabel de los Ángeles Arcos Vargas, Ángel |
| author |
Vega Márquez, Belén |
| author_facet |
Vega Márquez, Belén Rubio Escudero, Cristina Nepomuceno Chamorro, Isabel de los Ángeles Arcos Vargas, Ángel |
| author_role |
author |
| author2 |
Rubio Escudero, Cristina Nepomuceno Chamorro, Isabel de los Ángeles Arcos Vargas, Ángel |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
Lenguajes y Sistemas Informáticos Organización Industrial y Gestión de Empresas I TIC134: Sistemas Informáticos Ministerio de Ciencia, Innovación y Universidades (MICINN). España Junta de Andalucía |
| dc.subject.none.fl_str_mv |
Electricity price forecasting Deep learning Day-ahead market Time series forecasting |
| topic |
Electricity price forecasting Deep learning Day-ahead market Time series forecasting |
| description |
The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the model |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/11441/133920 https://doi.org/10.3390/app11136097 |
| url |
https://hdl.handle.net/11441/133920 https://doi.org/10.3390/app11136097 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Applied Sciences, 11 (13) TIN2017-88209-C2 US-1263341 P18-RT-2778 https://www.mdpi.com/2076-3417/11/13/6097 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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MDPI |
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MDPI |
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