dc.creator | Vega Márquez, Belén | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Nepomuceno Chamorro, Isabel de los Ángeles | es |
dc.creator | Arcos Vargas, Ángel | es |
dc.date.accessioned | 2022-06-01T09:47:18Z | |
dc.date.available | 2022-06-01T09:47:18Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Vega Márquez, B., Rubio Escudero, C., Nepomuceno Chamorro, I.d.l.Á. y Arcos Vargas, Á. (2021). Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market. Applied Sciences, 11 (13) | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/133920 | |
dc.description.abstract | 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 | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2 | es |
dc.description.sponsorship | Junta de Andalucía US-1263341 | es |
dc.description.sponsorship | Junta de Andalucía P18-RT-2778 | es |
dc.format | application/pdf | es |
dc.format.extent | 19 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Applied Sciences, 11 (13) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Electricity price forecasting | es |
dc.subject | Deep learning | es |
dc.subject | Day-ahead market | es |
dc.subject | Time series forecasting | es |
dc.title | Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I | es |
dc.relation.projectID | TIN2017-88209-C2 | es |
dc.relation.projectID | US-1263341 | es |
dc.relation.projectID | P18-RT-2778 | es |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/11/13/6097 | es |
dc.identifier.doi | 10.3390/app11136097 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sistemas Informáticos | es |
dc.journaltitle | Applied Sciences | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 13 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Junta de Andalucía | es |