Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system

Autores
Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
ABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.
Fil: Alarcón, Rodrigo Germán. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Eficiente y Control, Argentina.
Fil: Alarcón, Martín Alejandro. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Eficiente y Control, Argentina.
Fil: González, Alejandro H. Instituto de Desarrollo Tecnológico para la Industria Química (INTEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional del Litoral (UNL), Facultad de Ingeniería Química (FIQ), Santa Fe, Argentina.
Fil: Ferramosca, Antonio. Università degli studi di Bergamo, Italia.
Peer Reviewed
Materia
artificial neural networks
deep learning
disturbance prediction
economic model predictive control
long short-term memory
microgrid
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Attribution-NonCommercial-NoDerivs 2.5 Argentina
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/11992
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Artificial neural networks for energy demand prediction in an economic MPC-Based energy management systemAlarcón, Rodrigo GermánAlarcón, Martín AlejandroGonzález, Alejandro H.Ferramosca, Antonioartificial neural networksdeep learningdisturbance predictioneconomic model predictive controllong short-term memorymicrogridABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.Fil: Alarcón, Rodrigo Germán. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Eficiente y Control, Argentina.Fil: Alarcón, Martín Alejandro. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Eficiente y Control, Argentina.Fil: González, Alejandro H. Instituto de Desarrollo Tecnológico para la Industria Química (INTEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional del Litoral (UNL), Facultad de Ingeniería Química (FIQ), Santa Fe, Argentina.Fil: Ferramosca, Antonio. Università degli studi di Bergamo, Italia.Peer ReviewedInternational Journal of Robust and Nonlinear Control2024-12-19T19:53:19Z2024-10-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttp://hdl.handle.net/20.500.12272/11992https://doi.org/10.1002/rnc.7671enginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 2.5 Argentinahttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/Rodrigo G. Alarcón, Martín G. Alarcón, Alejandro H. González, Antonio Ferramoscae Licencia Creative Commons / CC BY-NC (Autoría – No Comercial)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:47:41Zoai:ria.utn.edu.ar:20.500.12272/11992instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:47:43.248Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
title Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
spellingShingle Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
Alarcón, Rodrigo Germán
artificial neural networks
deep learning
disturbance prediction
economic model predictive control
long short-term memory
microgrid
title_short Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
title_full Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
title_fullStr Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
title_full_unstemmed Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
title_sort Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
dc.creator.none.fl_str_mv Alarcón, Rodrigo Germán
Alarcón, Martín Alejandro
González, Alejandro H.
Ferramosca, Antonio
author Alarcón, Rodrigo Germán
author_facet Alarcón, Rodrigo Germán
Alarcón, Martín Alejandro
González, Alejandro H.
Ferramosca, Antonio
author_role author
author2 Alarcón, Martín Alejandro
González, Alejandro H.
Ferramosca, Antonio
author2_role author
author
author
dc.subject.none.fl_str_mv artificial neural networks
deep learning
disturbance prediction
economic model predictive control
long short-term memory
microgrid
topic artificial neural networks
deep learning
disturbance prediction
economic model predictive control
long short-term memory
microgrid
dc.description.none.fl_txt_mv ABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.
Fil: Alarcón, Rodrigo Germán. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Eficiente y Control, Argentina.
Fil: Alarcón, Martín Alejandro. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Eficiente y Control, Argentina.
Fil: González, Alejandro H. Instituto de Desarrollo Tecnológico para la Industria Química (INTEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional del Litoral (UNL), Facultad de Ingeniería Química (FIQ), Santa Fe, Argentina.
Fil: Ferramosca, Antonio. Università degli studi di Bergamo, Italia.
Peer Reviewed
description ABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.
publishDate 2024
dc.date.none.fl_str_mv 2024-12-19T19:53:19Z
2024-10-20
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12272/11992
https://doi.org/10.1002/rnc.7671
url http://hdl.handle.net/20.500.12272/11992
https://doi.org/10.1002/rnc.7671
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 2.5 Argentina
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Rodrigo G. Alarcón, Martín G. Alarcón, Alejandro H. González, Antonio Ferramosca
e Licencia Creative Commons / CC BY-NC (Autoría – No Comercial)
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 2.5 Argentina
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Rodrigo G. Alarcón, Martín G. Alarcón, Alejandro H. González, Antonio Ferramosca
e Licencia Creative Commons / CC BY-NC (Autoría – No Comercial)
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv International Journal of Robust and Nonlinear Control
publisher.none.fl_str_mv International Journal of Robust and Nonlinear Control
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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