Forecasting energy demand in quicklime manufacturing: a data-driven approach

Leon-Medina, Jersson X.; Fonseca Gonzalez, John Erick; Callejas Rodriguez, Nataly Yohana; González Niño, Mario Eduardo; Hernandez Moreno, Saul Andres; Pineda Muñoz, Wilman Alonso; Siachoque Celys, Claudia Patricia; Umbarila Suarez, Bernardo; Pozo Montero, Francesc|||0000-0001-8958-6789

Forecasting energy demand in quicklime manufacturing: a data-driven approach

This study presents a deep learning-based framework for forecasting energy demand in a quicklime production company, aiming to enhance operational efficiency and enable data-driven decision-making for industrial scalability. Using one year of real electricity consumption data, the methodology integr...

Descripción completa

Detalles Bibliográficos
Autores:	Leon-Medina, Jersson X., Fonseca Gonzalez, John Erick, Callejas Rodriguez, Nataly Yohana, González Niño, Mario Eduardo, Hernandez Moreno, Saul Andres, Pineda Muñoz, Wilman Alonso, Siachoque Celys, Claudia Patricia, Umbarila Suarez, Bernardo, Pozo Montero, Francesc\|\|\|0000-0001-8958-6789
Tipo de recurso:	artículo
Fecha de publicación:	2025
País:	España
Institución:	Universitat Politècnica de Catalunya (UPC)
Repositorio:	UPCommons. Portal del coneixement obert de la UPC
Idioma:	inglés
OAI Identifier:	oai:upcommons.upc.edu:2117/449464
Acceso en línea:	https://hdl.handle.net/2117/449464 https://dx.doi.org/10.3390/s25247632
Access Level:	acceso abierto
Palabra clave:	Energy consumption prediction Recurrent neural network Deep learning Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) Time series forecasting Àrees temàtiques de la UPC::Enginyeria civil::Aspectes econòmics

id	ES_b4f93b575c01f2d8d56e3d1c4a191c19
oai_identifier_str	oai:upcommons.upc.edu:2117/449464
network_acronym_str	ES
network_name_str	España
repository_id_str
spelling	Forecasting energy demand in quicklime manufacturing: a data-driven approachLeon-Medina, Jersson X.Fonseca Gonzalez, John ErickCallejas Rodriguez, Nataly YohanaGonzález Niño, Mario EduardoHernandez Moreno, Saul AndresPineda Muñoz, Wilman AlonsoSiachoque Celys, Claudia PatriciaUmbarila Suarez, BernardoPozo Montero, Francesc\|\|\|0000-0001-8958-6789Energy consumption predictionRecurrent neural networkDeep learningLong Short-Term Memory (LSTM)Gated Recurrent Unit (GRU)Time series forecastingÀrees temàtiques de la UPC::Enginyeria civil::Aspectes econòmicsThis study presents a deep learning-based framework for forecasting energy demand in a quicklime production company, aiming to enhance operational efficiency and enable data-driven decision-making for industrial scalability. Using one year of real electricity consumption data, the methodology integrates temporal and operational variables—such as load profile, active power, shift indicators, and production-related proxies—to capture the dynamics of energy usage throughout the manufacturing process. Several neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Conv1D models, were trained and compared to predict short-term power demand with 10-min resolution. Among these, the GRU model achieved the highest predictive accuracy, with a best performance of RMSE = 2.18 kW, MAE = 0.49 kW, and SMAPE = 3.64% on the test set. The resulting forecasts support cost-efficient scheduling under time-of-use tariffs and provide valuable insights for infrastructure planning, capacity management, and sustainability optimization in energy-intensive industries.Colombian Ministry of Science Innovation and Technology (MINISTERIO DE CIENCIA, TECNOLOGÌA E INNOVACIÓN-Minciencias) with its grant 934 “Convocatoria de estancias posdoctorales orientadas por misiones”. This research is also funded by FONDO FRANCISCO JOSÉ DE CALDAS.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20252025-12-1620252025-12-19journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/449464https://dx.doi.org/10.3390/s25247632reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4494642026-05-27T15:37:01Z
dc.title.none.fl_str_mv	Forecasting energy demand in quicklime manufacturing: a data-driven approach
title	Forecasting energy demand in quicklime manufacturing: a data-driven approach
spellingShingle	Forecasting energy demand in quicklime manufacturing: a data-driven approach Leon-Medina, Jersson X. Energy consumption prediction Recurrent neural network Deep learning Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) Time series forecasting Àrees temàtiques de la UPC::Enginyeria civil::Aspectes econòmics
title_short	Forecasting energy demand in quicklime manufacturing: a data-driven approach
title_full	Forecasting energy demand in quicklime manufacturing: a data-driven approach
title_fullStr	Forecasting energy demand in quicklime manufacturing: a data-driven approach
title_full_unstemmed	Forecasting energy demand in quicklime manufacturing: a data-driven approach
title_sort	Forecasting energy demand in quicklime manufacturing: a data-driven approach
dc.creator.none.fl_str_mv	Leon-Medina, Jersson X. Fonseca Gonzalez, John Erick Callejas Rodriguez, Nataly Yohana González Niño, Mario Eduardo Hernandez Moreno, Saul Andres Pineda Muñoz, Wilman Alonso Siachoque Celys, Claudia Patricia Umbarila Suarez, Bernardo Pozo Montero, Francesc\|\|\|0000-0001-8958-6789
author	Leon-Medina, Jersson X.
author_facet	Leon-Medina, Jersson X. Fonseca Gonzalez, John Erick Callejas Rodriguez, Nataly Yohana González Niño, Mario Eduardo Hernandez Moreno, Saul Andres Pineda Muñoz, Wilman Alonso Siachoque Celys, Claudia Patricia Umbarila Suarez, Bernardo Pozo Montero, Francesc\|\|\|0000-0001-8958-6789
author_role	author
author2	Fonseca Gonzalez, John Erick Callejas Rodriguez, Nataly Yohana González Niño, Mario Eduardo Hernandez Moreno, Saul Andres Pineda Muñoz, Wilman Alonso Siachoque Celys, Claudia Patricia Umbarila Suarez, Bernardo Pozo Montero, Francesc\|\|\|0000-0001-8958-6789
author2_role	author author author author author author author author
dc.subject.none.fl_str_mv	Energy consumption prediction Recurrent neural network Deep learning Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) Time series forecasting Àrees temàtiques de la UPC::Enginyeria civil::Aspectes econòmics
topic	Energy consumption prediction Recurrent neural network Deep learning Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) Time series forecasting Àrees temàtiques de la UPC::Enginyeria civil::Aspectes econòmics
description	This study presents a deep learning-based framework for forecasting energy demand in a quicklime production company, aiming to enhance operational efficiency and enable data-driven decision-making for industrial scalability. Using one year of real electricity consumption data, the methodology integrates temporal and operational variables—such as load profile, active power, shift indicators, and production-related proxies—to capture the dynamics of energy usage throughout the manufacturing process. Several neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Conv1D models, were trained and compared to predict short-term power demand with 10-min resolution. Among these, the GRU model achieved the highest predictive accuracy, with a best performance of RMSE = 2.18 kW, MAE = 0.49 kW, and SMAPE = 3.64% on the test set. The resulting forecasts support cost-efficient scheduling under time-of-use tariffs and provide valuable insights for infrastructure planning, capacity management, and sustainability optimization in energy-intensive industries.
publishDate	2025
dc.date.none.fl_str_mv	2025 2025-12-16 2025 2025-12-19
dc.type.none.fl_str_mv	journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv	info:eu-repo/semantics/article
format	article
dc.identifier.none.fl_str_mv	https://hdl.handle.net/2117/449464 https://dx.doi.org/10.3390/s25247632
url	https://hdl.handle.net/2117/449464 https://dx.doi.org/10.3390/s25247632
dc.language.none.fl_str_mv	Inglés eng
language_invalid_str_mv	Inglés
language	eng
dc.rights.none.fl_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv	Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv	reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC)
instname_str	Universitat Politècnica de Catalunya (UPC)
reponame_str	UPCommons. Portal del coneixement obert de la UPC
collection	UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_	1869417304588025856
score	15,811543

Forecasting energy demand in quicklime manufacturing: a data-driven approach

Similares en LA Referencia