Pronóstico de velocidad de viento para generación eólica Offshore basado en programación genética
| dc.contributor.advisor | Rivera Rodríguez, Sergio Raúl | |
| dc.contributor.author | Garrido Atencia, Oscar Alberto | |
| dc.contributor.researchgroup | Grupo de Investigación EMC-UN | spa |
| dc.date.accessioned | 2021-06-01T17:19:53Z | |
| dc.date.available | 2021-06-01T17:19:53Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Las energías renovables han surgido como la alternativa más viable para solucionar los problemas que presentan las fuentes de generación convencionales. En este sentido, la generación eólica offshore cuenta con gran potencial de crecimiento para los próximos años es por esto que el presente trabajo plantea una metodología que implementa la programación genética para realizar pronósticos de vientos promedio a mediano y largo plazo, con el fin de minimizar la incertidumbre asociada a este tipo de generación. Para esto, inicialmente se realiza el planteamiento del algoritmo regresión simbólica híbrida por medio del cual se realizarán los pronósticos de vientos propuestos; realizando una descripción del funcionamiento de este. Posteriormente se realiza la implementación del algoritmo planteado en cuatro casos de estudio ubicados en zonas costeras y en islas, de tal manera que se disponga de históricos de datos meteorológicos con los cuales poder realizar las pruebas del algoritmo. Posterior a esto, se evaluarán los errores obtenidos para seleccionar una cantidad de datos para entrenamiento y prueba del algoritmo. | spa |
| dc.description.abstract | Renewable energies have emerged as the most viable alternative to solve the problems presented by conventional generation sources. offshore wind generation has great growth potential for the next years, which is why this work proposes a methodology that implements genetic programming to make forecasts of average winds in the medium and long term, to minimize the uncertainty associated with this kind of generation. To that, initially the approach of the hybrid symbolic regression algorithm is carried out by means of which the proposed wind forecasts will be made; making a description of how it works. Subsequently, the implementation of the algorithm proposed in four case studies located in coastal areas and on islands is carried out, so that historical meteorological data are available with which to carry out the algorithm tests. After this, the errors obtained will be evaluated to select an amount of data for training and testing the algorithm. | eng |
| dc.description.degreelevel | Maestría | spa |
| dc.description.degreename | Magíster en Ingeniería - Ingeniería de Eléctrica | spa |
| dc.description.researcharea | Inteligencia Computacional aplicada al Sector Eléctrico | spa |
| dc.format.extent | 1 recurso en línea (82 páginas) | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/79586 | |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad Nacional de Colombia | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.department | Departamento de Ingeniería Eléctrica y Electrónica | spa |
| dc.publisher.faculty | Facultad de Ingeniería | spa |
| dc.publisher.place | Bogotá | spa |
| dc.publisher.program | Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Eléctrica | spa |
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| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.ddc | 620 - Ingeniería y operaciones afines | spa |
| dc.subject.proposal | Pronóstico | spa |
| dc.subject.proposal | Energía eólica | spa |
| dc.subject.proposal | Generación eólica offshore | spa |
| dc.subject.proposal | Programación genética | spa |
| dc.subject.proposal | Regresión simbólica | spa |
| dc.subject.proposal | Forecast | eng |
| dc.subject.proposal | Wind Power | eng |
| dc.subject.proposal | Offshore Wind Generation | eng |
| dc.subject.proposal | Genetic Programming | eng |
| dc.subject.proposal | Symbolic Regression | eng |
| dc.subject.unesco | Energía eólica | |
| dc.subject.unesco | Wind power | |
| dc.subject.unesco | Fuente de energía renovable | |
| dc.subject.unesco | Renewable energy sources | |
| dc.title | Pronóstico de velocidad de viento para generación eólica Offshore basado en programación genética | spa |
| dc.title.translated | Wind speed forecast for offshore wind generation based on genetic programming | eng |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/TM | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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