Short-Term Electricity Load Forecasting via CNN-KAN Heterogeneous Feature Mapping with Recursive Smoothing Soft B-Spline Basis Enhancement

Electricity load forecasting is essential for the optimal operation of smart grids and effective energy scheduling. However, increasing uncertainties caused by complex power system operations, renewable energy integration, and changes in user behavior have made load series more nonstationary and nonlinear. Traditional forecasting methods and existing deep learning models still have limitations in modeling such complex nonlinear relationships. To improve the nonlinear representation capability of short-term load forecasting models, this paper proposes a CNN-KAN framework based on heterogeneous feature mapping. The proposed method first employs a convolutional neural network to extract local temporal dependency features from multivariate historical load sequences. A Kolmogorov–Arnold Network is then used to perform adaptive nonlinear function mapping. In addition, a recursive smoothing soft B-spline basis mapping mechanism is introduced to improve the continuity and adaptability of the basis representation in KAN. Experiments are conducted on the Tetouan City electricity load dataset under two forecasting scenarios, namely Quads and Combined. The model is evaluated using MSE, RMSE, MAE, R2, and MAPE. Experimental results show that CNN-KAN achieves stable forecasting performance in both scenarios. The R2 values exceed 0.99, while the MAPE values remain below 1%. The proposed model demonstrates competitive overall forecasting performance compared with the Decision Tree, LSTM, TCN, and Informer baselines across the two forecasting scenarios. These results demonstrate the effectiveness of the proposed CNN-KAN heterogeneous feature mapping framework for short-term electricity load forecasting.