Approximating the solution of an economic MPC using artificial neural networks

dc.creatorAlarcón, Rodrigo Germán
dc.creatorAlarcón, Martín Alejandro
dc.creatorGonzález, Alejandro H.
dc.creatorFerramosca, Antonio
dc.creator.orcid0000-0001-9936-1452
dc.creator.orcid0000-0002-3823-043x
dc.creator.orcid0000-0001-9132-4577
dc.creator.orcid0000-0003-3935-9734
dc.date.accessioned2024-12-26T20:13:13Z
dc.date.issued2024-05-21
dc.description.abstractAbstract: Economic model predictive control is a recognized advanced control strategy which calculates control actions by solving an optimization problem in real time. The issue of numerical computation is the main barrier to implementing this type of controller. Deep learning has emerged as a promising solution to reduce the computational cost. This paper proposes a deep learning approximation of an Economic MPC, particularly with artificial neural networks, of the control strategy for managing energy resources in a residential microgrid. Operational data were generated from the solution established by the controller to train, validate and test the neural network using Matlab. Simulation results showed that the proposed approach can approximate the control strategy correctly.
dc.description.affiliationFil: Alarcón, Rodrigo Germán. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Electrónica y Control, Argentina.
dc.description.affiliationFil: Alarcón, Martín Alejandro. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Electrónica y Control, Argentina.
dc.description.affiliationFil: González, Alejandro H. Instituto de Desarrollo Tecnológico para la Industria Química (INTEC), CONICET - Universidad Nacional del Litoral (UNL), Argentina
dc.description.affiliationFil: Ferramosca, Antonio. Università degli Studi di Bergamo, Italia.
dc.formatpdf
dc.identifier.doi10.1109/RPIC59053.2023.10530691
dc.identifier.urihttp://hdl.handle.net/20.500.12272/12038
dc.language.isoen
dc.publisherIEEE Xplore
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Argentinaen
dc.rights.holderRodrigo G. Alarcón, Martín G. Alarcón, Alejandro H. González, Antonio Ferramosca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.rights.useLicencia Creative Commons / CC BY-NC (Autoría – No Comercial)
dc.subjectdeep learning
dc.subjectapproximate economic model pre-dictive control
dc.subjectartificial neural networks
dc.titleApproximating the solution of an economic MPC using artificial neural networks
dc.typeinfo:eu-repo/semantics/article
dc.type.versionpublisherVersion

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