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Wind Resource Evaluation in High-Mountain Ecosystems of the Andean Region Through Weibull Distributions and IDEAM Meteorological Record
Evaluación del recurso eólico en ecosistemas de alta montaña de la región andina mediante distribuciones de Weibull con datos meteorológicos del IDEAM
DOI:
https://doi.org/10.15446/sicel.v12.121188Palabras clave:
Wind Energy, Weibull distribution, high altitude ecosystems, data automation, meteorological analysis (en)Energía eólica, ecosistemas de páramo, automatización de datos, análisis meteorológico (es)
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In response to the challenges of the energy transition and the need to diversify Colombia’s electricity mix, this research project focused on studying the wind potential in high-mountain ecosystems in the Andean region, using meteorological data from IDEAM. The study looked at the possibility of generating small-scale electricity in isolated communities by applying the Weibull statistical distribution. A quantitative and exploratory approach was used. More than 70 data files were cleaned, filtered by percentiles, and validated. A Python-based tool was created to automate the data processing, distribution fitting, wind power estimation, and interactive visualization through a web application. In addition, data from NASA’s POWER API was included for comparison. The results showed that most stations had low wind potential (less than 50 W/m²), although some specific areas had better conditions and a better statistical fit using the NASA data.
En respuesta a los desafíos de la transición energética y la necesidad de diversificar la matriz eléctrica de Colombia, este proyecto de investigación se enfocó en caracterizar el potencial eólico en ecosistemas de alta montaña de la región andina, utilizando datos meteorológicos del IDEAM. El estudio evaluó la viabilidad de generación eléctrica a pequeña escala en comunidades aisladas mediante la aplicación de la distribución estadística de Weibull. Se adoptó un enfoque cuantitativo y exploratorio. Más de 70 archivos fueron limpiados, filtrados por percentiles y validados. Se desarrolló una herramienta en Python capaz de automatizar el procesamiento, ajuste de distribución, estimación de potencia eólica y visualización interactiva a través de una aplicación web. Además, se incorporaron datos de la API POWER de la NASA para comparaciones complementarias. Los resultados mostraron que la mayoría de estaciones presentan bajo potencial (menos de 50 W/m²), aunque se identificaron zonas con mejores condiciones y mayor ajuste en registros de la NASA.
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Derechos de autor 2025 Franky Yoan Cely Quesada, Nicolas Hernan Castillo García, Alonso de Jesús Retamoso Llamas, Fabio Alfonso González, Ingrid Johanna Moreno Celis, Edgar Efrain Niño Velandia
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
