基于多尺度并行特征融合与Kolmogorov-Arnold网络重构的短期电力负荷预测OA

Addressing the strong nonlinearity of short-term power loads and the deficits of insufficient feature interaction and linear reconstruction errors in traditional decomposition-ensemble models,this paper proposes a short-term load forecasting model based on multi-scale parallel feature fusion and Kolmogorov-Arnold network(KAN)reconstruction.First,a feature optimization and signal decomposition strategy combining(Pearson correlation coefficient-maximal information coefficient(PCC-MIC)correlation analysis and variational mode decomposition(VMD)is established to filter redundant meteorological features and mitigate sequence non-stationarity.Second,a dual-channel architecture is designed for parallel extraction using Informer and temporal convolutional network(TCN).Then,an adaptive gated fusion mechanism(AGFM)learns time-varying weights to dynamically regulate feature attention of different time steps for precise multi-scale fusion.Finally,KAN is introduced to replace conventional linear output layers.Leveraging learnable B-spline activation functions,KAN enables adaptive nonlinear mapping from high-dimensional fused features to load values.Case studies demonstrate that the proposed model significantly outperforms mainstream baselines in prediction accuracy,offering a reliable reference for power system planning and stable operation.