Predicción de demanda eléctrica en instalaciones industriales y residenciales utilizando aprendizaje automático combinado
DOI:
https://doi.org/10.17533/udea.redin.20200584Palabras clave:
energía, pronóstico, inteligencia artificialResumen
Este artículo presenta modelos de pronóstico de demanda eléctrica industriales y residencial, aplicando aprendizaje automático combinado. El pronóstico de demanda eléctrica a corto plazo beneficia a consumidores y proveedores, ya que permite mejorar las políticas de eficiencia energética y el uso racional de los recursos. Se desarrollan modelos de inteligencia computacional para el pronóstico diario de demanda eléctrica y una estrategia híbrida para construir el modelo de pronóstico diario basado en modelos para la próxima hora. Se aplican tres métodos de preprocesamiento de datos: tratamiento de valores perdidos, eliminación de valores atípicos y estandarización. Se aplica extracción de características para reducir el sobreajuste y el tiempo de entrenamiento, mejorando la precisión. El mejor modelo se optimiza mediante búsqueda de grilla en el espacio de hiperparámetros. Luego se genera un conjunto de 24 instancias para construir el modelo de pronóstico completo para el día siguiente. Las técnicas aplicadas se desarrollan y evalúan en el Centro Nacional de Supercomputación (Cluster-UY), Uruguay. Se utilizan tres conjuntos de datos reales para la evaluación: un parque industrial en Burgos (España), la demanda eléctrica total de Uruguay y la demanda de una subestación de distribución en Montevideo (Uruguay). Se aplican métricas estándar para evaluar los modelos propuestos. Los resultados indican que el mejor modelo, basado en ExtraTreesRegressor, tiene un error porcentual medio de 2, 55% en datos industriales, 5, 17% en consumo total y 9, 09% en subestación.
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