Machine Learning Model for Primary Solar Resource Assessment in Colombia

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dc.creatorObando Paredes, Edgar Darío
dc.date2023-12-29
dc.date.accessioned2025-10-01T23:53:10Z
dc.descriptionThis work introduces a Machine Learning (ML) model designed to predict solar radiation in diverse cities representing Colombia's climatic variability. It is crucial to assert that the amount of solar energy received in a specific region is directly related to solar radiation and its availability, which is influenced by each area's particular climatic and geographic conditions. Due to the high variability and resulting uncertainty, various approaches have been explored, including the use of numerical models to estimate solar radiation. The primary objective of this study was to develop and validate an ML model that accurately predicts solar radiation in cities. The methodology employed was specific to data treatment and ML model development. It was structured into three fundamental stages: clustering, estimation, and response, considering that the model is based on historical data. The obtained results were assessed using appropriate statistical definitions, not only determining the model's efficiency in terms of prediction but also considering interactions between data for the approximation and prediction of solar radiation. In this context, it is crucial to emphasize that the research contributes to understanding solar radiation in Colombia. This study underscores the importance of developing ML models to predict solar radiation, emphasizing the need to consider the country's climatic diversity. The results obtained, following the model's application, provide valuable information for comprehending and anticipating the availability of this primary resource.en-US
dc.descriptionEn este trabajo se presenta un modelo de Aprendizaje Automático (ML por sus siglas en inglés) diseñado para predecir la radiación solar en diversas ciudades que representan la variabilidad climática de Colombia. Destaca afirmar, que la cantidad de energía solar recibida en una región específica está directamente relacionada con la radiación solar y su disponibilidad, la cual se ve afectada por las condiciones climáticas y geográficas particulares de cada área. Ante la alta variabilidad e incertidumbre resultante, se han explorado diversos enfoques, entre ellos, el uso de modelos numéricos para estimar la radiación solar. El objetivo principal de este estudio fue desarrollar y validar un modelo ML que permita predecir con precisión la radiación solar en las ciudades. La metodología empleada fue propia del tratamiento de datos y desarrollo de modelos ML. Se estructuró en tres etapas fundamentales: agrupamiento, estimación y respuesta, al tener en cuenta que el modelo está estructurado con base en datos históricos. Los resultados obtenidos fueron evaluados mediante definiciones estadísticas apropiadas, que no solo determinaron la eficiencia del modelo en términos de predicción, sino que también consideraron las interacciones entre datos para la aproximación y predicción de la radiación solar. En este sentido, es crucial señalar que la investigación contribuye al entendimiento de la radiación solar en el contexto colombiano. Este estudio subraya la importancia de desarrollar modelos ML para predecir la radiación solar, destacando la necesidad de considerar la diversidad climática del país. Los resultados obtenidos, tras la aplicación del modelo, proporcionan información valiosa para comprender y anticipar la disponibilidad de este recurso primario.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2789
dc.identifier10.22430/22565337.2789
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7881
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
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dc.rightsDerechos de autor 2023 TecnoLógicases-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceTecnoLógicas; Vol. 26 No. 58 (2023); e2789en-US
dc.sourceTecnoLógicas; Vol. 26 Núm. 58 (2023); e2789es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectAprendizaje automáticoes-ES
dc.subjectenergía renovablees-ES
dc.subjectmodelo predictivoes-ES
dc.subjectpredicción climáticaes-ES
dc.subjectradiación solares-ES
dc.subjectMachine learningen-US
dc.subjectrenewable energyen-US
dc.subjectpredictive modelen-US
dc.subjectclimate predictionen-US
dc.subjectsolar radiationen-US
dc.titleMachine Learning Model for Primary Solar Resource Assessment in Colombiaen-US
dc.titleModelo de aprendizaje automático para la evaluación del recurso solar primario en Colombiaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

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