¿Cuáles son los beneficios de aplicar el aprendizaje automático («Machine Learning») en el sector energético?

En la actualidad, las empresas del sector energético se han quedado atrás cuando se trata de adoptar las nuevas oportunidades que ofrece la Inteligencia Artificial. Las últimas innovaciones tecnológicas, tales como el análisis predictivo de última generación, es algo que las empresas están dejando pasar de manera desapercibida. No están extrayendo el máximo valor de los datos para sacarle provecho a la información.

A través del aprendizaje automático, las empresas pueden convertir realizar análisis avanzados en previsión con el fin de mejorar la toma de decisiones. Esto les permite lograr mejoras reales y mensurables en comparación con los métodos tradicionales. Muchos sectores ya han adoptado el aprendizaje automático, pero el sector de la energía se está quedando atrás.

Una empresa que demuestra el éxito de aprovechar esta tecnología es SWHERE, los cuales son líderes mundialmente reconocidos en el campo del aprendizaje automático. Han sacado provecho en distintos sectores como la producción de energía eólica, la distribución de gas, agua al por menor y distribución de poder.

Todos nos preguntamos: ¿qué es exactamente lo que puede hacer el aprendizaje automático en términos de predicción y pronóstico?

Un buen ejemplo en la parte de producción de energía eólica es la planificación de la producción. Esto es difícil en parte porque los parques eólicos suelen estar en el mar o en áreas abiertas, donde el clima puede cambiar en un instante, haciendo el pronostico complicado. Como resultado, rara vez se tienen predicciones suficientemente precisas, lo que resulta una pérdida de ingresos.

Al planificar la producción de energía eólica, se tienen que responder las siguientes preguntas: ¿Cuándo habrá viento, qué tan poderoso será y en qué dirección soplará? Queremos saber si hay un patrón en el viento que no habíamos visto antes. Para encontrar estas conexiones usamos nuevos algoritmos. En este caso, se proporcionaron muchos datos, tanto meteorológicos como de producción y pronósticos.

Al aplicar técnicas de análisis predictivo se logro reducir la incertidumbre en la producción de energía eólica en más de un 45%, lo que ocasionó que los costos de desequilibrio disminuyeran a la mitad.

En el análisis tradicional, los analistas trabajan con datos, mientras que en el aprendizaje automático, los datos trabajan para usted. El aprendizaje automático a menudo tiene la flexibilidad de cambiar el modelo original con solo unos pocos parámetros.

Link: http://www.swhere.com/

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