Machine Learning-based Multi-Class Traffic Management for Smart Grid Communication Network.

Smart grid communication networks are facing the increasing challenge of heterogeneous facilities and diverse communication requirements. Traditional communication technologies without the ability to adapt cannot meet these requirements. A data-driven and machine learning-based multi-class traffic management scheme is proposed in this study, which classifies network traffic into different service levels for better transmission provision. Numerical experiments with a real-world dataset are conducted to validate the effectiveness of the multi-class traffic management scheme, in which XGBoost achieves an accuracy of 0.9842 and an F1 score of 0.9914.