Abstract: The accuracy of electric vehicle(EV) charging load prediction is significantly influenced by the coupling relationship between charging prices and multi-source loads,yet traditional prediction models often neglect this dynamic interaction.To address this,this paper proposes a novel charging load prediction method based on multi-branch fusion and a multi-head attention residual network.Firstly,historical charging loads,charging prices,and temporal features are dynamically decoupled to construct a price-load decoupled feature space.Secondly,a multi-branch parallel network is designed to separately extract decoupled temporal features,price-sensitive features,and spatial correlation features,while cross-branch feature interaction is achieved through a multi-head self-attention mechanism.Finally,residual connections are introduced to optimize gradient propagation and mitigate degradation in deep networks.Experimental results based on real-world charging station data demonstrate that the proposed method reduces the mean absolute error by 16.15% compared to benchmark models such as CNN-BiLSTM and GRU.This approach provides high-precision prediction support for power system dispatching and validates the synergistic effectiveness of price-decoupled features and attention mechanisms in load forecasting.

Key words: Electric vehicles, Load forecasting, Charging price, Feature decoupling, Multi-branch fusion, Multi-attention residuals