Modelos de inteligencia artificial para la predicción del crecimiento de la demanda eléctrica en sistemas urbanos de Riobamba, Ecuador
DOI:
https://doi.org/10.63688/ek1dhd94Palabras clave:
inteligencia artificial, predicción de demanda eléctrica, redes neuronales, modelos predictivos, planificación energéticaResumen
El incremento sostenido del consumo eléctrico en ciudades intermedias como Riobamba ha evidenciado la necesidad de emplear herramientas predictivas más precisas que permitan optimizar la planificación del sistema energético. En este contexto, la investigación tuvo como objetivo analizar el desempeño de modelos de inteligencia artificial en la estimación del comportamiento futuro de la demanda eléctrica, utilizando datos históricos del periodo 2020–2024. El estudio se desarrolló bajo un enfoque cuantitativo de tipo aplicado, con un diseño no experimental de carácter longitudinal, basado en el análisis de series temporales. Los datos fueron sometidos a procesos de depuración, normalización y segmentación en conjuntos de entrenamiento, validación y prueba. Posteriormente, se implementaron modelos de redes neuronales, máquinas de soporte vectorial y un enfoque híbrido, desarrollados en un entorno de simulación computacional y evaluados mediante indicadores de error. Los resultados evidenciaron un alto nivel de precisión en las predicciones. Las redes neuronales presentaron el mejor desempeño, con errores mínimos de 1.8 % y un promedio cercano al 2.5 %. En comparación, el modelo híbrido y las máquinas de soporte vectorial alcanzaron errores de 2.1 % y 2.7 %, respectivamente. Estos resultados confirman la eficacia de la inteligencia artificial para modelar comportamientos no lineales y estacionales.
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Derechos de autor 2026 Samantha Marlene Puente Bosquez, Carlos Isaac Machuca Valverde, Danner Anderson Figueroa Guerra, Josué Lenin Fuentes Véliz (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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