Inteligencia Artificial para la Optimización de Redes Eléctricas en Latinoamérica
DOI:
https://doi.org/10.70577/213ara83Palabras clave:
Inteligencia Artificial , Machine Laerning, Modelos, Optimización, Redes.Resumen
El artículo explora la transformación de las redes eléctricas en América Latina mediante la integración de la inteligencia artificial (IA). Ante una demanda energética que se triplicará para 2050, la IA se vuelve crucial para optimizar la eficiencia, confiabilidad e integración de energías renovables. Países como Brasil, México y Chile lideran esta adopción, utilizando IA para gestionar la distribución, equilibrar la oferta y demanda, y mejorar la fiabilidad de la red.El estudio destaca que los modelos de regresión lineal predicen la eficiencia energética con alta precisión (R2 =0.86), influenciados por el consumo, generación y condiciones meteorológicas. Los modelos de clasificación de optimización alcanzan una precisión cercana al 100% , mientras que la clasificación de riesgo muestra resultados mixtos, con dificultades en clases minoritarias, sugiriendo la necesidad de balanceo de datos. El clustering K-Means identificó tres segmentos geográficos de la red con distintas características operativas y de mantenimiento. Los modelos ARIMA y LSTM demuestran una robusta capacidad para predecir la demanda y el consumo energético, capturando patrones temporales complejos. La optimización lineal demostró un balance efectivo en la distribución de energía entre diversas fuentes, y se identificó el potencial de algoritmos heurísticos para futuras mejoras.A pesar de los desafíos como el desequilibrio de clases en los datos de riesgo, la necesidad de modelos de predicción de fallas más robustos y la integración dinámica de datos, la IA ofrece un camino prometedor hacia redes más eficientes, resilientes y con mayor satisfacción del cliente.
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