An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread...

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Autores:
Cantillo-Luna, Sergio
Moreno-Chuquen, Ricardo
López Sotelo, Jesús Alfonso
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
spa
OAI Identifier:
oai:red.uao.edu.co:10614/15861
Acceso en línea:
https://hdl.handle.net/10614/15861
https://red.uao.edu.co/
Palabra clave:
Decision making
Deep learning
Electricity price forecasting (EPF)
Probabilistic forecasting
Time series forecasting
Rights
openAccess
License
Derechos reservados -MDPI, 2023
id REPOUAO2_d9a802e1f4db26b6370d0f455947f82a
oai_identifier_str oai:red.uao.edu.co:10614/15861
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
title An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
spellingShingle An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
Decision making
Deep learning
Electricity price forecasting (EPF)
Probabilistic forecasting
Time series forecasting
title_short An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
title_full An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
title_fullStr An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
title_full_unstemmed An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
title_sort An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
dc.creator.fl_str_mv Cantillo-Luna, Sergio
Moreno-Chuquen, Ricardo
López Sotelo, Jesús Alfonso
dc.contributor.author.none.fl_str_mv Cantillo-Luna, Sergio
Moreno-Chuquen, Ricardo
López Sotelo, Jesús Alfonso
dc.subject.proposal.eng.fl_str_mv Decision making
Deep learning
Electricity price forecasting (EPF)
Probabilistic forecasting
Time series forecasting
topic Decision making
Deep learning
Electricity price forecasting (EPF)
Probabilistic forecasting
Time series forecasting
description This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector
publishDate 2023
dc.date.issued.none.fl_str_mv 2023
dc.date.accessioned.none.fl_str_mv 2024-10-15T14:50:02Z
dc.date.available.none.fl_str_mv 2024-10-15T14:50:02Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.eng.fl_str_mv Text
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.eng.fl_str_mv Cantillo-Luna, S.; Moreno-Chuquen, R.; Lopez-Sotelo, J.; Celeita, D. (2023). An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture. Energies. 16(19). https://doi.org/10.3390/en16196767
dc.identifier.issn.spa.fl_str_mv 19961073
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/15861
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
identifier_str_mv Cantillo-Luna, S.; Moreno-Chuquen, R.; Lopez-Sotelo, J.; Celeita, D. (2023). An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture. Energies. 16(19). https://doi.org/10.3390/en16196767
19961073
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
url https://hdl.handle.net/10614/15861
https://red.uao.edu.co/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.citationendpage.spa.fl_str_mv 24
dc.relation.citationissue.spa.fl_str_mv 19
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 16
dc.relation.ispartofjournal.spa.fl_str_mv Energies
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spelling Cantillo-Luna, SergioMoreno-Chuquen, RicardoLópez Sotelo, Jesús Alfonsovirtual::5727-12024-10-15T14:50:02Z2024-10-15T14:50:02Z2023Cantillo-Luna, S.; Moreno-Chuquen, R.; Lopez-Sotelo, J.; Celeita, D. (2023). An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture. Energies. 16(19). https://doi.org/10.3390/en1619676719961073https://hdl.handle.net/10614/15861Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector24 páginasapplication/pdfspaMDPIBasel, SwitzerlandDerechos reservados -MDPI, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network ArchitectureArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852419116Energies1. Cantillo-Luna, S.; Moreno-Chuquen, R.; Lopez-Sotelo, J.A. Intra-day Electricity Price Forecasting Based on a Time2Vec-LSTM Neural Network Model. 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Available online: https://github.com/cerlymarco/keras-hypetune (accessed on 12 April 2023)Decision makingDeep learningElectricity price forecasting (EPF)Probabilistic forecastingTime series forecastingComunidad generalPublicationfc227fb1-22ec-47f0-afe7-521c61fddd32virtual::5727-1fc227fb1-22ec-47f0-afe7-521c61fddd32virtual::5727-1https://scholar.google.com.au/citations?user=7PIjh_MAAAAJ&hl=envirtual::5727-10000-0002-9731-8458virtual::5727-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000249106virtual::5727-1ORIGINALAn_Intra-Day_Electricity_Price_Forecasting_Based_on_a_Probabilistic_Transformer_Neural_Network_Architecture.pdfAn_Intra-Day_Electricity_Price_Forecasting_Based_on_a_Probabilistic_Transformer_Neural_Network_Architecture.pdfArchivo texto completo del artículo de revista, PDFapplication/pdf3025208https://red.uao.edu.co/bitstreams/14cdacbe-3f8f-4cdf-afb4-9782233d8f0e/download1b755b5c2ad8bcda401e7e7ba771f714MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81672https://red.uao.edu.co/bitstreams/b15fb3a7-853f-4090-81b8-5975c610d89f/download6987b791264a2b5525252450f99b10d1MD52TEXTAn_Intra-Day_Electricity_Price_Forecasting_Based_on_a_Probabilistic_Transformer_Neural_Network_Architecture.pdf.txtAn_Intra-Day_Electricity_Price_Forecasting_Based_on_a_Probabilistic_Transformer_Neural_Network_Architecture.pdf.txtExtracted texttext/plain82033https://red.uao.edu.co/bitstreams/284ac3af-f900-4200-bc9d-e7cdc5054056/downloadede4b5769b654bc8b41c5e4a0da000c4MD53THUMBNAILAn_Intra-Day_Electricity_Price_Forecasting_Based_on_a_Probabilistic_Transformer_Neural_Network_Architecture.pdf.jpgAn_Intra-Day_Electricity_Price_Forecasting_Based_on_a_Probabilistic_Transformer_Neural_Network_Architecture.pdf.jpgGenerated Thumbnailimage/jpeg15495https://red.uao.edu.co/bitstreams/63af79d2-27bb-4a9c-84b8-13fdc60cd05c/downloadcc1ae123ed7a720d6d8e760774940769MD5410614/15861oai:red.uao.edu.co:10614/158612024-10-16 03:00:41.968https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados -MDPI, 2023open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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