Urban Wind Energy Assessment Using Machine Learning and Multi-criteria Analysis

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dc.creatorGarrido-Silva, Gianina
dc.creatorRamírez-Murillo, Harrynson
dc.creatorCajamarca-García, María Paula
dc.creatorTorres-Pinzón, Carlos Andrés
dc.date2025-06-30
dc.date.accessioned2025-10-01T23:53:15Z
dc.descriptionThe transition toward renewable energy sources in urban environments presents technical and strategic challenges due to the variability of available resources and the inherent constraints of built-up spaces. This study assessed the potential for wind energy generation in Bogotá, D.C., Colombia, applying data science techniques and machine learning models. Clustering analysis, the Analytic Hierarchy Process (AHP), and Weibull distribution modeling used historical meteorological data on wind speed and temperature. The unsupervised analysis identified three representative hourly wind patterns, while the Weibull distribution estimated an operational wind speed of 2.2 m/s. The AHP method facilitated the selection of wind turbines based on technical criteria such as cut-in speed, rated power, and blade number. Among the key results, the 300 X-300 turbine achieved a Capacity Factor (CF) of 25.43%, a Yield Ratio (Yr) of 0.1352 h, and a Performance Ratio (PR) of 43.49%. These indicators reveal that, despite the moderate wind potential during specific periods, the low energy density and high variability limit the technical feasibility of wind systems in the study area. It is concluded that integrating data analysis tools with multi-criteria decision-making methods provides a robust framework for assessing urban wind resources and establishes a solid foundation for the tailored design of sustainable energy solutions.en-US
dc.descriptionLa transición hacia fuentes de energía renovable en entornos urbanos representa un desafío técnico y estratégico, debido a la variabilidad de los recursos disponibles y a las restricciones propias de los espacios construidos. Esta investigación evaluó el potencial de generación de energía eólica en Bogotá D.C., Colombia, mediante la aplicación de técnicas de ciencia de datos y modelos de aprendizaje automático. Se empleó un análisis de clustering, el Proceso de Jerarquía Analítica (AHP, por sus siglas en inglés) y un análisis de distribución de Weibull, utilizando datos meteorológicos históricos de velocidad del viento y temperatura. Este análisis no supervisado permitió identificar tres patrones horarios representativos del comportamiento eólico, mientras que la distribución de Weibull estimó una velocidad de operación de 2.2 m/s. El método AHP facilitó la selección de turbinas eólicas con base en criterios técnicos como velocidad de arranque, potencia nominal y número de palas. Entre los resultados más relevantes, la turbina 300 X- 300 alcanzó un factor de capacidad de 25.43 %, una relación de producción (Yr) de 0.1352 h y un índice de desempeño del 43.49 %. Estos índices revelaron que, aunque existen periodos con potencial eólico moderado, la baja densidad energética y la alta variabilidad limitan la viabilidad técnica de estos sistemas en el área objeto de estudio. Se concluye que la integración de herramientas de análisis de datos con métodos de decisión multicriterio permite una evaluación robusta del recurso eólico urbano, y ofrece una base sólida para el diseño de soluciones energéticas sostenibles hechas a la medida.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3262
dc.identifier10.22430/22565337.3262
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7928
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3262/3680
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3262/3771
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3262/3772
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dc.rightsDerechos de autor 2025 TecnoLógicases-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceTecnoLógicas; Vol. 28 No. 63 (2025); e3262en-US
dc.sourceTecnoLógicas; Vol. 28 Núm. 63 (2025); e3262es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectclustering analysisen-US
dc.subjectmachine learningen-US
dc.subjectwind energyen-US
dc.subjectdata preprocessingen-US
dc.subjectsystem performanceen-US
dc.subjecthierarchical systemsen-US
dc.subjectanálisis de clusteringes-ES
dc.subjectaprendizaje automáticoes-ES
dc.subjectenergía eólicaes-ES
dc.subjectpreprocesamiento de datoses-ES
dc.subjectrendimiento del sistemaes-ES
dc.subjectsistemas jerárquicoses-ES
dc.titleUrban Wind Energy Assessment Using Machine Learning and Multi-criteria Analysisen-US
dc.titleEvaluación del potencial eólico urbano mediante aprendizaje automático y análisis multicriterioes-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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