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dc.contributor.authorHernández Hernández, César
dc.contributor.authorRodríguez Díaz, Francisco
dc.contributor.authorMoreno Úbeda, José Carlos
dc.contributor.authorDa Costa Mendes, Paulo Renato
dc.contributor.authorNormey Rico, Julio Elías
dc.contributor.authorGuzmán Sánchez, José Luis
dc.date.accessioned2020-01-16T12:32:38Z
dc.date.available2020-01-16T12:32:38Z
dc.date.issued2017-06-30
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10835/7418
dc.description.abstractElectricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmodelinges_ES
dc.subjectforecastinges_ES
dc.subjectenergy hubses_ES
dc.subjectneural networkses_ES
dc.subjectmodel predictive controles_ES
dc.titleThe Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Managementes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/10/7/884es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional