Modelo de aprendizaje automático para la evaluación del recurso solar primario en Colombia
Resumen
En 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.
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