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dc.contributor.advisorNiño Ruiz, Elías David
dc.contributor.advisorCapacho Portilla, José Rafael
dc.contributor.authorCalabria Sarmiento, Juan Carlos
dc.date.accessioned2020-09-29T20:44:20Z
dc.date.available2020-09-29T20:44:20Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/10584/9012
dc.description.abstractThis research proposes a 4D-Var ensemble-based data assimilation framework for wind energy potentialestimation. In this formulation, in the 4D-Var context, the intrinsic need of adjoint models is avoided via the use of an ensemble of model realizations. These ensembles are employed to build control spaces onto which analysis increments are estimated. Control spaces are built via a modified Cholesky decomposition. The particular structure of this estimator allows for a matrix-free implementation of the proposed filter formulation. Experimental tests are performed,making use of wind turbines catalogs and the Atmospheric General Circulation Model Speedy. The results reveal that our proposed framework can properly estimate wind energy potential capacities within reasonable accuracies in terms of Root-Mean-Square-Error, and even more,these estimations are better than those of traditional 4D-Var ensemble-based methods. Besides, Wind Turbine Generators(WTGs) with low rate-capacity are the ones which provide homogeneous behavior of error estimations around the globe. As the rate-capacity increases,the potential energy increases as well, but the error dispersion of ensemble members grow, which can difficult decision-makingprocesses. Of course, rate-capacity is just a single parameter of many in the WTG context, and we do not consider, for instance, economic aspects in our study, which can be crucial for deciding whether or not to employ green sources of energy.
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherUniversidad del Nortees_ES
dc.subject.lcshToma de decisiones -- Métodos de simulación
dc.subject.lcshEnergía eólica -- Métodos de simulación -- Investigaciones.
dc.titleWind Energy Potential Estimation via a Hybrid Data Assimilation Methodes_ES
dc.typeTrabajo de grado - Maestríaes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.publisher.programDoctorado en Ingeniería de Sistemas y Computaciónes_ES
dc.publisher.departmentDepartamento de ingeniería de sistemases_ES
dc.description.degreelevelDoctoradoes_ES
dc.publisher.placeBarranquilla, Colombiaes_ES
dc.rights.creativecommonshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcces_ES
dc.type.driverinfo:eu-repo/semantics/masterThesises_ES
dc.type.contentTextes_ES
dc.type.versioninfo:eu-repo/semantics/updatedVersiones_ES
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaes_ES
dc.description.degreenameDoctor en Ingeniería de Sistemas y Computaciónes_ES
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2es_ES
dcterms.audience.educationalcontextPúblico generales_ES
dcterms.audience.professionaldevelopmentDoctoradoes_ES


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