Approximating the solution of an economic MPC using artificial neural networks
dc.creator | Alarcón, Rodrigo Germán | |
dc.creator | Alarcón, Martín Alejandro | |
dc.creator | González, Alejandro H. | |
dc.creator | Ferramosca, Antonio | |
dc.creator.orcid | 0000-0001-9936-1452 | |
dc.creator.orcid | 0000-0002-3823-043x | |
dc.creator.orcid | 0000-0001-9132-4577 | |
dc.creator.orcid | 0000-0003-3935-9734 | |
dc.date.accessioned | 2024-12-26T20:13:13Z | |
dc.date.issued | 2024-05-21 | |
dc.description.abstract | Abstract: 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.affiliation | Fil: Alarcón, Rodrigo Germán. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Electrónica y Control, Argentina. | |
dc.description.affiliation | Fil: Alarcón, Martín Alejandro. Universidad Tecnológica Nacional, Facultad Regional Reconquista, Grupo de Investigación en Programación Electrónica y Control, Argentina. | |
dc.description.affiliation | Fil: González, Alejandro H. Instituto de Desarrollo Tecnológico para la Industria Química (INTEC), CONICET - Universidad Nacional del Litoral (UNL), Argentina | |
dc.description.affiliation | Fil: Ferramosca, Antonio. Università degli Studi di Bergamo, Italia. | |
dc.format | ||
dc.identifier.doi | 10.1109/RPIC59053.2023.10530691 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12272/12038 | |
dc.language.iso | en | |
dc.publisher | IEEE Xplore | |
dc.rights | Attribution-NonCommercial-NoDerivs 2.5 Argentina | en |
dc.rights.holder | Rodrigo G. Alarcón, Martín G. Alarcón, Alejandro H. González, Antonio Ferramosca | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ | |
dc.rights.use | Licencia Creative Commons / CC BY-NC (Autoría – No Comercial) | |
dc.subject | deep learning | |
dc.subject | approximate economic model pre-dictive control | |
dc.subject | artificial neural networks | |
dc.title | Approximating the solution of an economic MPC using artificial neural networks | |
dc.type | info:eu-repo/semantics/article | |
dc.type.version | publisherVersion |
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