KAN–CNN: A Novel Framework for Electric Vehicle Load Forecasting with Enhanced Engineering Applicability and Simplified Neural Network Tuning

Abstract

Electric Vehicle (EV) load forecasting is critical for optimizing resource allocation and ensuring the stability of modern energy systems. However, traditional machine learning models, predominantly based on Multi-Layer Perceptrons (MLPs), encounter substantial challenges in modeling the complex, nonlinear, and dynamic patterns inherent in EV charging data, often leading to overfitting and high computational costs. To overcome these limitations, this study introduces KAN–CNN, a novel hybrid architecture that integrates Kolmogorov–Arnold Networks (KANs) into traditional machine learning frameworks, specifically Convolutional Neural Networks (CNNs). By combining the spatial feature extraction strength of CNNs with the adaptive nonlinearity of KAN, KAN–CNN achieves superior feature representation and modeling flexibility. The key innovations include bottleneck KAN convolutional layers for reducing parameter complexity, Self-Attention Kolmogorov–Arnold Network with Global Nonlinearity (Self-KAGN) Attention to enhance global dependency modeling, and Focal KAGN Modulation for dynamic feature refinement. Furthermore, regularization techniques such as L1/L2 penalties, dropout, and Gaussian noise injection are utilized to enhance the model’s robustness and generalization capability. When applied to EV load forecasting, KAN–CNN demonstrates prediction accuracy comparable to state-of-the-art methods while significantly reducing computational overhead and simplifying parameter tuning. This work bridges the gap between theoretical innovations and practical applications, offering a robust and efficient solution for dynamic energy system challenges.