电力系统保护与控制2026，Vol.54Issue(12)：176-187,12.DOI:10.19783/j.cnki.pspc.251388
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
Short-term power load forecasting based on multi-scale parallel feature fusion and Kolmogorov-Arnold network reconstruction
摘要
Abstract
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.关键词
负荷预测/Informer/时间卷积网络/自适应门控融合机制/Kolmogorov-Arnold网络Key words
load forecasting/Informer/temporal convolutional network/adaptive gated fusion mechanism/Kolmogorov-Arnold network引用本文复制引用
于永进,鉴奕霖,刘琪,矫文书..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[J].电力系统保护与控制,2026,54(12):176-187,12.基金项目
This work is supported by the Youth Fund of National Natural Science Foundation of China(No.52307115). 国家自然科学基金青年项目资助(52307115) （No.52307115）
山东省自然科学基金项目资助(ZR2022ME219) （ZR2022ME219）
