Forecasting the global solar radiation in Nariño – Colombia
figuras, símbolos, tablas
- Autores:
-
Hoyos Gómez, Laura Sofía
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/79679
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
620 - Ingeniería y operaciones afines
Electrificación rural
Energía solar
Radiación solar
Rural electrification
Solar energy
Solar radiation
Community participation
Rural electrification
Analytic Hierarchy Process
Multicriteria Approach
Energy projects
Human Development Index
Sustainable Development Goal Index
Temperature based models
Data imputation
Hargreaves and Samani
Spatial interpolation techniques
solar radiation mapping
Proyectos energéticos
Participación comunitaria
Electrificación rural
Proceso Analítico Jerárquico
Índice de Desarrollo Humano
Índice de Metas de Desarrollo Sostenible
Modelos basados en temperatura
Imputación de datos
Hargreaves y Samani
Técnicas de interpolación espacial
Mapeo de radiación solar
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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|
| dc.title.eng.fl_str_mv |
Forecasting the global solar radiation in Nariño – Colombia |
| dc.title.translated.spa.fl_str_mv |
Pronóstico de la radiación solar global en Nariño - Colombia |
| title |
Forecasting the global solar radiation in Nariño – Colombia |
| spellingShingle |
Forecasting the global solar radiation in Nariño – Colombia 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 620 - Ingeniería y operaciones afines Electrificación rural Energía solar Radiación solar Rural electrification Solar energy Solar radiation Community participation Rural electrification Analytic Hierarchy Process Multicriteria Approach Energy projects Human Development Index Sustainable Development Goal Index Temperature based models Data imputation Hargreaves and Samani Spatial interpolation techniques solar radiation mapping Proyectos energéticos Participación comunitaria Electrificación rural Proceso Analítico Jerárquico Índice de Desarrollo Humano Índice de Metas de Desarrollo Sostenible Modelos basados en temperatura Imputación de datos Hargreaves y Samani Técnicas de interpolación espacial Mapeo de radiación solar |
| title_short |
Forecasting the global solar radiation in Nariño – Colombia |
| title_full |
Forecasting the global solar radiation in Nariño – Colombia |
| title_fullStr |
Forecasting the global solar radiation in Nariño – Colombia |
| title_full_unstemmed |
Forecasting the global solar radiation in Nariño – Colombia |
| title_sort |
Forecasting the global solar radiation in Nariño – Colombia |
| dc.creator.fl_str_mv |
Hoyos Gómez, Laura Sofía |
| dc.contributor.advisor.none.fl_str_mv |
Ruiz Mendoza, Belizza Janet |
| dc.contributor.author.none.fl_str_mv |
Hoyos Gómez, Laura Sofía |
| dc.contributor.researcher.none.fl_str_mv |
Patricio Mendoza Araya José Francisco Ruiz Muñoz |
| dc.contributor.researchgroup.spa.fl_str_mv |
GIPEM - Grupo de Investigación en Potencia, Energía y Mercados |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 620 - Ingeniería y operaciones afines |
| topic |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 620 - Ingeniería y operaciones afines Electrificación rural Energía solar Radiación solar Rural electrification Solar energy Solar radiation Community participation Rural electrification Analytic Hierarchy Process Multicriteria Approach Energy projects Human Development Index Sustainable Development Goal Index Temperature based models Data imputation Hargreaves and Samani Spatial interpolation techniques solar radiation mapping Proyectos energéticos Participación comunitaria Electrificación rural Proceso Analítico Jerárquico Índice de Desarrollo Humano Índice de Metas de Desarrollo Sostenible Modelos basados en temperatura Imputación de datos Hargreaves y Samani Técnicas de interpolación espacial Mapeo de radiación solar |
| dc.subject.ocde.none.fl_str_mv |
Electrificación rural Energía solar Radiación solar Rural electrification Solar energy Solar radiation |
| dc.subject.proposal.eng.fl_str_mv |
Community participation Rural electrification Analytic Hierarchy Process Multicriteria Approach Energy projects Human Development Index Sustainable Development Goal Index Temperature based models Data imputation Hargreaves and Samani Spatial interpolation techniques solar radiation mapping Proyectos energéticos |
| dc.subject.proposal.spa.fl_str_mv |
Participación comunitaria Electrificación rural Proceso Analítico Jerárquico Índice de Desarrollo Humano Índice de Metas de Desarrollo Sostenible Modelos basados en temperatura Imputación de datos Hargreaves y Samani Técnicas de interpolación espacial Mapeo de radiación solar |
| description |
figuras, símbolos, tablas |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-06-22T20:17:41Z |
| dc.date.available.none.fl_str_mv |
2021-06-22T20:17:41Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/79679 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/79679 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
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Nuevos métodos de valoración: modelos multicriterio. Retrieved from http://medcontent.metapress.com/index/A65RM03P4874243N.pdf Badosa, J., Gobert, E., Grangereau, M., & Kim, D. (2017). Day-Ahead Probabilistic Forecast of Solar Irradiance: A Stochastic Differential Equation Approach. In P. Drobinski, M. Mougeot, D. Picard, R. Plougonven, & P. Tankov (Eds.), Mathematics & statistics (p. 22). Paris: Springer. Retrieved from http://www.springer.com/series/10533 Bakirci, K. (2009). Models of solar radiation with hours of bright sunshine: A review. doi: 10.1016/j.rser.2009 .07.011 Balbás Egea, J. ff., & Eguren Egiguren, J. A. (2019). Bases for a sustainable energy model. Case study: Basque autonomous community. International Journal of Sustainable Energy, 38(9), 884–903. doi: 10 .1080/14786451.2019.1609474 Benali, L., Notton, G., Fouilloy, A., Voyant, C., & Dizene, R. (2019). 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Environmental Modelling and Software, 16(4), 309– 330. doi: 10.1016/S1364-8152(01)00008-1 Kipp & Zonen. (2000). Instruction Manual Pyranometer/ Albedometer CM11 e CM14. Kiš, I. M. (2016). Comparison of Ordinary and Universal Kriging interpolation techniques on a depth variable (a case of linear spatial trend), case study of the Šandrovac Field. The Mining-Geology-Petroleum Engineering Bulletin, 31(2), 41–58. doi: 10.17794/rgn.2016.2.4 Kleinbaum, D. G., & Klein, M. (2010). Logistic Regression: a self-learning text (No. 3). Springer. doi: 10.1007/978-1-4419-1742-3 Konstantin, P., & Konstantin, M. (2018). Power and Energy Systems Engineering Economics. Gewerbestrasse: Springer. doi: https://doi.org/10.1007/978-3-319-72383-9References 109 Kwon, B. S., Park, R. J., & Song, K. B. (2020). Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer. Journal of Electrical Engineering and Technology, 15(4), 1501–1509. Retrieved from https://doi.org/10.1007/s42835-020-00424-7 doi: 10.1007/s42835-020-00424-7 Layanun, V., Suksamosorn, S., & Songsiri, J. (2017). Missing-data Imputation for Solar Irradiance Forecasting in Thailand. In Sice annual conference (pp. 1234–1239). Kanazawa. Li, H., Cao, F., Wang, X., & Ma, W. (2014). A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China. The Scientific World Journal, 2014. doi: doi.org/10.1155/2014/ 128754 Li, J., & Heap, A. D. (2008). A Review of Spatial Interpolation Methods for Environmental Scientists. Australian Geological Survey Organisation, 68(2008/23), 154. doi: http://www.ga.gov.au/image{\_}cache/ GA12526.pdf Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3-4), 228–241. 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The R Journal, 9(1), 207–218. Retrieved from https://cran.r-project.org/web/packages/ imputeTS/vignettes/imputeTS-Time-Series-Missing-Value-Imputation-in-R.pdf Mossos, ff. A. (2019). Informe mensual de telemetría (Tech. Rep.). Centro Nacional de Monitoreo.References 111 Munawar, U., & Wang, Z. (2020). A Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting. Journal of Electrical Engineering and Technology, 15(2), 561–569. Retrieved from https://doi.org/10.1007/s42835-020-00346-4 doi: 10.1007/s42835-020-00346-4 National Oceanic and Atmospheric Administration. (2020). Climate Prediction Center. Retrieved from https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ ONI_v5.php Nwokolo, S. C., & Ogbulezie, J. C. (2017, 5). A quantitative review and classification of empirical models for predicting global solar radiation in West Africa. Beni-Suef University Journal of Basic and Applied Sciences. 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Lowering electricity access barriers by means of participative processes applied to microgrid solutions : The Chilean case. Proceedings of the IEEE, 1–15. doi: 10.1109/JPROC.2019 .2922342112 References Paulescu, M. (2008). Solar Irradiation via Air Temperature Data. In V. Badescu (Ed.), Modeling solar radiation at the earth surface (pp. 175–193). Springer. Paulescu, M., Paulescu, E., Gravila, P., & Badescu, V. (2013). Weather Modeling and Forecasting of PV Systems Operation. London: Springer London. Retrieved from http://link.springer.com/10.1007/ 978-1-4471-4649-0 doi: 10.1007/978-1-4471-4649-0 Pebesma, E. (2016). Fitting variogram models in gstat. Retrieved from https://www.r-spatial.org/ r/2016/02/14/gstat-variogram-fitting.html Pebesma, E., & Graeler, B. (2020). Package ’gstat’ Title Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation (Tech. Rep.). Retrieved from https://github.com/r-spatial/gstat/ issues/ Premalatha, N., & Valan Arasu, A. (2016). 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Retrieved from https://repositorio.cepal.org/ bitstream/handle/11362/43156/1/S1800010_es.pdf Reikard, G. (2009). Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, 83(3), 342–349. Retrieved from http://dx.doi.org/10.1016/j.solener.2008 .08.007 doi: 10.1016/j.solener.2008.08.007 Rivero, M., Orozco, S., Sellschopp, F. S., & Loera-Palomo, R. (2017). A new methodology to extend the validity of the Hargreaves-Samani model to estimate global solar radiation in different climates: Case study Mexico. Renewable Energy. doi: 10.1016/j.renene.2017.08.003 Rodriguez, H. (2011). Observatorio de energías renovables en América Latina y el Caribe: Colombia. OLADE; ONUDI. Rodríguez-Rivero, C., Pucheta, J., Laboret, S., Sauchelli, V., & Patiño, D. (2017). Short-Term Series Forecasting By Complete and Incomplete Datasets. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 5–16.References 113 Saaty, T. L. (1990). How tomake a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. doi: 10.1016/0377-2217(90)90057-I Saaty, T. L. (2008). Decision making with the analytic hierarchy process. Int. J. Servces Sciences, 1(1), 83. Retrieved from http://www.rafikulislam.com/uploads/resourses/ 197245512559a37aadea6d.pdf doi: 10.1504/IJSSCI.2008.017590 Samani, Z. (2000). Estimating Solar Radiation and Evapotranspiration Using Minimum Climatological Data. Journal of Irrigation and Drainage Engineering, 126, 265–267. doi: 10.1061/(ASCE)0733 -9437(2000)126:4(265) Sandia National Laboratories. (2021). Irradiance & Insolation. Retrieved from https://pvpmc.sandia .gov/modeling-steps/1-weather-design-inputs/irradiance-and-insolation-2/ Sankar, G., Kumar, P., & Maiti, R. (2018). Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon ( SOC ). Journal of the Saudi Society of Agricultural Sciences, 17(2), 114–126. Retrieved from https://doi.org/10.1016/j.jssas.2016.02.001 doi: 10.1016/ j.jssas.2016.02.001 Şen, Z. (2008). Solar Energy Fundamentals and Modeling Techniques. Springer. doi: 10.1007/978-1-84800 -134-3 Serrano, A., Sanchez, G., & Cancillo, M. L. (2015). Correcting daytime thermal offset in unventilated pyranometers. Journal of Atmospheric and Oceanic Technology, 32(11), 2088–2099. doi: 10.1175/JTECH -D-15-0058.1 Shumway, R. H., & Stoffer, D. S. (2011). Time Series Analysis and Its Applications With R Examples (Vol. 102; G. Casella, S. Fienberg, & I. Olkin, Eds.). Springer. Retrieved from http://books.google.com/ books?id=9tv0taI8l6YC doi: 10.1007*978-1-4419-7865-3 Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018, 1). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459–497. Retrieved from https://www.sciencedirect.com/science/article/pii/S0196890417310622 doi: 10 .1016/J.ENCONMAN.2017.11.019 Suehrcke, H. (2000). 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Procedia - Social and Behavioral Sciences, 156(November 2014), 506–512. doi: 10.1016/ j.sbspro.2014.11.230 Zore, ff., Čuček, L., Širovnik, D., Novak Pintarič, Z., & Kravanja, Z. (2018). Maximizing the sustainability net present value of renewable energy supply networks. Chemical Engineering Research and Design, 131, 245–265. doi: 10.1016/j.cherd.2018.01.035 |
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Universidad Nacional de Colombia |
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Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática |
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Departamento de Ingeniería Eléctrica y Electrónica |
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Facultad de Ingeniería y Arquitectura |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ruiz Mendoza, Belizza Janet1af6ff1f67c365888bbcb9f0f9e01182Hoyos Gómez, Laura Sofía631cc61e8b774a6ea6bff7f86ed8d6a2Patricio Mendoza ArayaJosé Francisco Ruiz MuñozGIPEM - Grupo de Investigación en Potencia, Energía y Mercados2021-06-22T20:17:41Z2021-06-22T20:17:41Z2021https://repositorio.unal.edu.co/handle/unal/79679Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/figuras, símbolos, tablasIntroducing the community to technical projects requires a deal with the social, energy and environmental policies as well as the cultural field. To address an energy project from a socio-technical view requires the joint analysis of both the project and the community. This work focuses on the formulation of a methodology to ease the prioritization of projects and community participation. To evaluate the community, the Human Development Index and Sustainable Development Goal Index are adjusted to the context and available information of Nariño. The Net Present Value is used for the project evaluation. The Analytic Hierarchy Process allows for the evaluation of the community and project jointly and establishing prioritization objectives. Moreover, the co-construction methodology is the basis to formulate guidelines to work with the community. This research found that there is a relationship between the projects that seek to improve the quality of the life and education in Nariño. Solar irradiance is a worldwide available resource that could drive electrification processes in regions with low socio-economic indexes. Therefore, to know solar irradiance behavior and data is increasingly a mandatory activity. However, some interesting sites, generally socio-economic outcast places, do not rely on solar irradiance data, and if information exists, it is not complete. Therefore, researchers use some techniques to estimate this energy resource with information from other meteorological variables as temperature. Nevertheless, there is not a broad analysis of these techniques in tropical and mountainous environments. Therefore, this research analyzes the performance of three well-known empirical temperature-based models in tropical and mountainous environments. Moreover, this work proposes a new empirical technique that models solar irradiance in some areas better than the three techniques mentioned. Statistical error comparison allows us to choose the best model for each location and the data imputation model. Hargreaves and Samani's model presented better results in the Pacific zone, and the proposed model showed better results in the Andean and Amazon zones. Another significant result is the linear relationship between the new empirical model constants and the altitude 2.500 MASL. The solar energy potential maps are an enabler for solar energy use. However, the lack of solar irradiance information is a barrier to elaborating on this type of decision tool. This research proposed the estimation of solar irradiance using air temperature data to increase the sampled points with the Hargreaves and Samani and a proposed empirical model. Also, the leave-one-out cross-validation is the technique used to assess the performance of four spatial interpolation techniques in a tropical and mountainous environment. The information came from Nariño state in Colombian that covers an area of \(33.268 km^{2}\) . The proposed empirical model shows better performance in sites with an altitude above 2.500 MASL, located in the Andean and Amazon zone. Further, Ordinary Kriging was the interpolation technique with the best behavior. Accurate mechanisms for forecasting solar irradiance boost solar energy applications. There are several techniques to forecast global solar irradiance, such as numerical weather prediction and statistical techniques. In this context, this research compare four forecasting approaches Autoregressive Integrated Moving Average, Single Layer Feed Forward Network, Multiple Layer Feed Forward Network, and Long Short-Term Memory in a one-day ahead horizon using incomplete datasets measured in a tropical and mountainous environment. The results show that the neural network-based models outperform the ARIMA model. Furthermore, LSTM has better performance with a low number of input data and in cloudiness environments.Incluir a las comunidades en proyectos socio-técnicos require abordar aspectos sociales, energéticos, ambientales, políticos y culturales. Dirigir un proyecto energético con un enfoque socio-técnico require el análisis en conjunto del proyecto y la comunidad impactada. En este sentido, este trabajo se enfoca en formular una metodología que facilite la priorización de proyectos y la participación de la comunidad. Para evaluar a la comunidad se adpatan los índices de desarrollo humano y los índices de los objetivos de desarrollo sustentable a la información disponible para Nariño. El valor presente neto es la herramienta usada para la evaluación del proyecto.El proceso de análisis jerárquico permite evaluar la comunidad y el proyecto conjuntamente y establecer objetivos de priorización. Por otra parte, la metodología de co-costrucción es la base de la directriz propuesta para trabajo con la comunidad. Esta investigación encontró que existe una relación entre los proyectos que buscan mejorar la calidad de vida y la educación en Nariño. La irradiancia solar es un recurso ampliamente disponible en el planeta, que podría contribuir al proceso de electrificación en lugares con bajos índices socio económicos. No obstante, en algunos lugares, la información de este recurso no está disponible o tiene baja calidad. Para superar este problema algunos investigadores han desarrollado técnicas para estimar la irradiancia solar. Una de esas técnicas son los modelos empíricos basados en temperatura para estimar el recurso. Sin embargo, no hay un amplio análisis del comportamiento de esas técnicas en ambientes tropicales y montañosos. Por lo tanto, esta investigación analiza el comportamiento de tres modelos empíricos basados en temperatura y un modelo propuesto bajo estas condiciones ambientales. Los errores estadísticos calculados permiten elegir el mejor modelo para cada punto evaluado. Con este modelo se hace la imputación de datos con el fin de incrementar la calidad de las bases de datos analizadas. El modelo propuesto se ajusta mejor a la zona Andina y amazónica, mientras el modelo de Hargreaves y Samani tiene mejores resultados en la zona Pacífica. Además, el modelo propuesto presenta una relación lineal entre las constantes empíricas y la altitud de las estaciones meteorológicas localizadas por encima de los 2.500 msnm. Los mapas que muestran el potencial de la energía solar facilitan el uso del recurso solar. Sin embargo, la falta de información de irradiancia solar son una barrera para elaborar este tipo de herramientas. Este investigación propone estimar la irradiancia global solar con datos de temperatura usando el modelo empírico de Hargreaves y Samaani y uno propuesto, para incrementar el número de puntos muestreados. Además, se implementa la técnica de validación cruzada conocida como dejar uno por fuera para evaluar el rendimiento de cuatro técnicas de interpolacióne espacial en un ambiente tropical y montañoso. La información usada es del departamento de Nariño-Colombia que tiene un área de 33.268 $km^{2}$. El modelo propuesto muestra un mejor comportamiento en sitios localizado a más de 2.500 msmnl, ubicados en la zona Andina y Amazonica. Además, Kriging ordinario es la mejor técnica de interpolación espacial. Los modelos de pronóstico de irradiancia solar impulsan las aplicaciones que usan energía solar. Existen varias técnicas para pronosticar la irradiancia solar global, como las númericas y las estadísticas. En este contexto, esta investigación compara cuatro enfoques de pronóstico estadístico: Promedio móvil integrado autorregresivo, red neuronal de una capa, red neuronal de multiples capas y memoria a corto y plazo, en un horizonte de un día por delante, utilizando conjuntos de datos incompletos medidos en un entorno tropical y montañoso. Los resultados muestran que los modelos basados en redes neuronales superan al modelo ARIMA. Además, LSTM tiene un mejor rendimiento con un número reducido de datos de entrada y en entornos de nubosidad.Alianza del PacíficoCentro de Energía - Universidad de ChileLa autora incluye versión en español de la tesis.DoctoradoDoctora en IngenieríaLa Alianza del Pacífico financió la estancia de investigación de 6 meses en la Universidad de Chile. -- El Centro de Energía de la Universidad de Chile financió el viaje para realizar el trabajo de campo en la localidad de Huatacondo.Meteorología Energética, Energía Solar131 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación620 - Ingeniería y operaciones afinesElectrificación ruralEnergía solarRadiación solarRural electrificationSolar energySolar radiationCommunity participationRural electrificationAnalytic Hierarchy ProcessMulticriteria ApproachEnergy projectsHuman Development IndexSustainable Development Goal IndexTemperature based modelsData imputationHargreaves and SamaniSpatial interpolation techniquessolar radiation mappingProyectos energéticosParticipación comunitariaElectrificación ruralProceso Analítico JerárquicoÍndice de Desarrollo HumanoÍndice de Metas de Desarrollo SostenibleModelos basados en temperaturaImputación de datosHargreaves y SamaniTécnicas de interpolación espacialMapeo de radiación solarForecasting the global solar radiation in Nariño – ColombiaPronóstico de la radiación solar global en Nariño - ColombiaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06TextColombiaNariñoAbdullah, L., & Najib, L. 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Chemical Engineering Research and Design, 131, 245–265. doi: 10.1016/j.cherd.2018.01.035Fundación CEIBALICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79679/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.unal.edu.co/bitstream/unal/79679/4/license_rdf24013099e9e6abb1575dc6ce0855efd5MD54ORIGINAL1081594025.2021.pdf1081594025.2021.pdfTesis de Doctorado en Ingeniería - Línea de Investigación en Automáticaapplication/pdf4699621https://repositorio.unal.edu.co/bitstream/unal/79679/5/1081594025.2021.pdf61fd08a7d898cc68919b12b2a6309793MD551081594025.2021 V.ESPAÑOL.pdf1081594025.2021 V.ESPAÑOL.pdfVersión en españolapplication/pdf5999585https://repositorio.unal.edu.co/bitstream/unal/79679/6/1081594025.2021%20V.ESPA%c3%91OL.pdf8bfcfffaf8e780c12f02ce9312dfe148MD56THUMBNAIL1081594025.2021.pdf.jpg1081594025.2021.pdf.jpgGenerated 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