Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada

Esta tesis presenta un modelo híbrido para el pronóstico de la velocidad del viento con alta resolución temporal y horizonte de largo plazo, diseñado para abordar fenómenos atmosféricos locales en una región costera tropical. La metodología combina transformadas armónicas (Fourier), análisis estadís...

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Autores:
Vega Zúñiga, Samuel José
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2025
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/14338
Acceso en línea:
https://hdl.handle.net/11323/14338
https://repositorio.cuc.edu.co/
Palabra clave:
Pronóstico
Regresión lineal
Heurística
Velocidad del viento
Energía eólica teórica
Fourier
DOE-ANOVA
PSO
Forecasting
Linear regression
Heuristic
Wind speed
Theorical wind energy
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
id RCUC2_37243a14fe32c33ece6901dc144ad322
oai_identifier_str oai:repositorio.cuc.edu.co:11323/14338
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
title Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
spellingShingle Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
Pronóstico
Regresión lineal
Heurística
Velocidad del viento
Energía eólica teórica
Fourier
DOE-ANOVA
PSO
Forecasting
Linear regression
Heuristic
Wind speed
Theorical wind energy
title_short Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
title_full Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
title_fullStr Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
title_full_unstemmed Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
title_sort Pronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariada
dc.creator.fl_str_mv Vega Zúñiga, Samuel José
dc.contributor.advisor.none.fl_str_mv Ospino Castro, Adalberto Jose
Rueda Bayona, Juan Gabriel
dc.contributor.author.none.fl_str_mv Vega Zúñiga, Samuel José
dc.contributor.jury.none.fl_str_mv Peña Gallardo, Rafael
Sousa Santos, Vladimir
Balbis Morejon, Milen
dc.subject.proposal.spa.fl_str_mv Pronóstico
Regresión lineal
Heurística
Velocidad del viento
Energía eólica teórica
topic Pronóstico
Regresión lineal
Heurística
Velocidad del viento
Energía eólica teórica
Fourier
DOE-ANOVA
PSO
Forecasting
Linear regression
Heuristic
Wind speed
Theorical wind energy
dc.subject.proposal.eng.fl_str_mv Fourier
DOE-ANOVA
PSO
Forecasting
Linear regression
Heuristic
Wind speed
Theorical wind energy
description Esta tesis presenta un modelo híbrido para el pronóstico de la velocidad del viento con alta resolución temporal y horizonte de largo plazo, diseñado para abordar fenómenos atmosféricos locales en una región costera tropical. La metodología combina transformadas armónicas (Fourier), análisis estadístico multivariable (DOE–ANOVA) y optimización heurística mediante Enjambre de Partículas (PSO), permitiendo construir una función pronosticadora físicamente coherente y computacionalmente replicable. El estudio se fundamenta en tres objetivos específicos: (i) analizar la influencia de temperatura, presión y humedad sobre la velocidad del viento mediante DOE-ANOVA, (ii) caracterizar armónicos relevantes a partir de su atenuación, amplificación y desfase, y (iii) optimizar parámetros armónicos con PSO para construir una serie pronosticada. Se utilizaron más de 40 años de datos horarios del conjunto ERA5 y datos in situ de la estación meteorológica de la Universidad de la Costa, Barranquilla. El modelo logró un MAE de 1.586, un MSE de 2.014 y un coeficiente de correlación cercano a 0.70 en un horizonte de pronóstico de cuatro años. A partir de esta serie se estimó el potencial energético teórico del viento, con una potencia promedio de 160.78 W/m² y una energía total acumulada de 5633.58 kWh/m². Estos resultados refuerzan la aplicabilidad del modelo como herramienta preliminar para estudios de viabilidad energética en regiones no instrumentadas. La aproximación propuesta prioriza la trazabilidad metodológica, la interpretación física del fenómeno y el enfoque energético, contribuyendo a la planificación sostenible de recursos eólicos.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-07-28T16:18:49Z
dc.date.available.none.fl_str_mv 2025-07-28T16:18:49Z
dc.date.issued.none.fl_str_mv 2025-06-24
dc.type.none.fl_str_mv Trabajo de grado - Doctorado
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.none.fl_str_mv Text
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/TD
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/14338
dc.identifier.instname.none.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.none.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.none.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/14338
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv spa
language spa
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ospino Castro, Adalberto JoseRueda Bayona, Juan GabrielVega Zúñiga, Samuel JoséPeña Gallardo, RafaelSousa Santos, VladimirBalbis Morejon, Milen2025-07-28T16:18:49Z2025-07-28T16:18:49Z2025-06-24https://hdl.handle.net/11323/14338Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Esta tesis presenta un modelo híbrido para el pronóstico de la velocidad del viento con alta resolución temporal y horizonte de largo plazo, diseñado para abordar fenómenos atmosféricos locales en una región costera tropical. La metodología combina transformadas armónicas (Fourier), análisis estadístico multivariable (DOE–ANOVA) y optimización heurística mediante Enjambre de Partículas (PSO), permitiendo construir una función pronosticadora físicamente coherente y computacionalmente replicable. El estudio se fundamenta en tres objetivos específicos: (i) analizar la influencia de temperatura, presión y humedad sobre la velocidad del viento mediante DOE-ANOVA, (ii) caracterizar armónicos relevantes a partir de su atenuación, amplificación y desfase, y (iii) optimizar parámetros armónicos con PSO para construir una serie pronosticada. Se utilizaron más de 40 años de datos horarios del conjunto ERA5 y datos in situ de la estación meteorológica de la Universidad de la Costa, Barranquilla. El modelo logró un MAE de 1.586, un MSE de 2.014 y un coeficiente de correlación cercano a 0.70 en un horizonte de pronóstico de cuatro años. A partir de esta serie se estimó el potencial energético teórico del viento, con una potencia promedio de 160.78 W/m² y una energía total acumulada de 5633.58 kWh/m². Estos resultados refuerzan la aplicabilidad del modelo como herramienta preliminar para estudios de viabilidad energética en regiones no instrumentadas. La aproximación propuesta prioriza la trazabilidad metodológica, la interpretación física del fenómeno y el enfoque energético, contribuyendo a la planificación sostenible de recursos eólicos.This doctoral thesis presents a hybrid model for wind speed forecasting with high temporal resolution and a long-term horizon, designed to address local atmospheric phenomena in a tropical coastal region. The methodology integrates harmonic transforms (Fourier), multivariable statistical analysis (DOE–ANOVA), and heuristic optimization via Particle Swarm Optimization (PSO), enabling the construction of a physically consistent and computationally replicable forecasting function. The research is guided by three specific objectives: (i) to analyze the influence of temperature, pressure, and humidity on wind speed through DOE–ANOVA; (ii) to characterize relevant harmonics by their attenuation, amplification, and phase shift; and (iii) to optimize harmonic parameters using PSO to generate a forecast series. Over 40 years of hourly ERA5 reanalysis data and in situ records from the Universidad de la Costa meteorological station in Barranquilla were used to train and validate the model. The model achieved a MAE of 1.586, an MSE of 2.014, and a correlation coefficient close to 0.70 over a four-year forecasting horizon. Based on the forecasted series, the theoretical wind energy potential was estimated, yielding an average power of 160.78 W/m² and a total accumulated energy of 5633.58 kWh/m². These findings support the model's usefulness as a preliminary tool for energy feasibility studies in non-instrumented regions. The proposed approach emphasizes methodological traceability, physical interpretation of the phenomenon, and an energy-oriented perspective, contributing to sustainable wind resource planning.Lista de tablas y figuras 10--Introducción 13--Planteamiento del problema 13--Justificación 16--Objetivo general 19--Objetivos específicos 19--Aporte del trabajo 20--Estado del arte y marco teórico 21--Estado del arte 22--Tipos de modelos: horizontes de pronóstico y aplicaciones 22--Aplicación de DOE–ANOVA en el estudio del comportamiento del viento 30--Modelos híbridos basados en heurísticas para el pronóstico del viento 32--Integración de heurística y estadística multivariada 37--Fundamentos teóricos 42--Diseño de experimento y análisis de varianza 42--Fundamentos del análisis de fourier 43--Filtro de butterworth 54--Filtro de chebyshev Tipo I 56--Filtro de chebyshev tipo II 58--Función exponencial decreciente 61--Función relación señal a ruido (SNR) 62--Función por tramos con un factor de amplificación – 65--Función por tramos con un factor de amplificación logarítmica 66--Función de cambio de fase senoidal 69--Función de cambio de fase cosenoidal 69--Función de cambio de fase logarítmica 70--Fundamentos de la heurística PSO para modelación híbrida 70--Potencial energético del viento 72--Metodología 74--Análisis de fourier 77--Construcción del modelo híbrido 79--Validación del modelo 82--Estimación del potencial energético a partir del pronóstico 83--Resultados y discusión 85--Resultados del diseño de experimentos y análisis de varianza 86--DOE-ANOVA 86--Regresión múltiple 96--Resultados del modelado matemático en la atenuación, cambio de fase y amplificación de armónicos de fourier 99--Atenuación 99-Amplificación 107--Cambio de fase 112--Resultados del pronóstico de la velocidad del viento 115--Datos del pronóstico 115--Espectros de frecuencias y variabilidad climática 120--Modelación 124--Pronóstico 125--Validación 134--Estimación del potencial energético Teórico 139--Discusión, conclusiones finales y recomendaciones 140--Discusión general de los resultados 141--Análisis estadístico mediante DOE-ANOVA 141--Modelado matemático armónico 142--Pronóstico de la velocidad del viento con PSO 143--Conclusiones del estudio 144--Recomendaciones para investigaciones futuras 146--Referencias 147Doctor(a) en Ingenieria EnergéticaDoctorado169 páginasapplication/pdfspaCorporacion Universidad de la CostaEnergiaBarranquilla, ColombiaDoctorado en Ingenieria EnergéticaCorporación Universidad de la CostaPronóstico de viento local con alta resolución y amplio horizonte mediante modelación híbrida: heurística y estadística multivariadaTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06Textinfo:eu-repo/semantics/doctoralThesishttp://purl.org/redcol/resource_type/TDinfo:eu-repo/semantics/acceptedVersionAbbasipour, M., Igder, M. 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Informacion Tecnologica, 27(3), 23–34. https://doi.org/10.4067/S0718-07642016000300004PronósticoRegresión linealHeurísticaVelocidad del vientoEnergía eólica teóricaFourierDOE-ANOVAPSOForecastingLinear regressionHeuristicWind speedTheorical wind energyPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/568ea0a9-f687-42fe-9cbd-ee9f42168bd1/download73a5432e0b76442b22b026844140d683MD51ORIGINALPronóstico de Viento Local con Alta Resolución y Amplio Horizonte Mediante Moderacion.cleaned (1) (2).pdfPronóstico de Viento Local con Alta Resolución y Amplio Horizonte Mediante Moderacion.cleaned (1) (2).pdfapplication/pdf4567352https://repositorio.cuc.edu.co/bitstreams/f482e866-7e3b-46ab-9e8a-a6ab0af2ad53/download275e30661597f39087cc1546538def1aMD52TEXTPronóstico de Viento Local con Alta Resolución y Amplio Horizonte Mediante Moderacion.cleaned (1) (2).pdf.txtPronóstico de Viento Local con Alta Resolución y Amplio Horizonte Mediante 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>


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