Volume 60

Mamba-Driven Transformer-LSTM Collaborative Architecture for Charging station Load Forecasting Xiao Hu, Zezhen Zhang, Hongjie Zhu, Yang Gao, Chuang Wang, Shiping Jin, Jinduo Yang, Jiaquan Yang

Abstract

Charging stations, as essential infrastructure for smart scheduling and energy management in smart cities, significantly improve the operational efficiency and intelligence of urban energy systems. This study proposes a load forecasting model that integrates LSTM and Transformer networks, introducing the Mamba module as a critical bridge to fuse temporal features extracted by both models, thereby constructing a superior knowledge representation to enhance prediction accuracy. First, LSTM and Transformer networks are used separately to extract latent temporal features from the load data. Subsequently, the Mamba module fuses and enhances these features to capture finer-grained information. Comprehensive experimental results demonstrate that the proposed method achieves an average prediction accuracy of 93.4% on data from multiple electric vehicle charging stations, without substantially increasing computational overhead.

Keywords Charging station load forecasting, Intelligent scheduling, Energy management, LSTM, Transformer, Mamba