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

Weiwei Jiang, Haoyu Han, Miao He, Weixi Gu

Abstract: 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.