A Glass-Box Deep-Learning Method for Electrical Energy System Modeling Based on Kolmogorov–Arnold Network

Deep-learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their convenience and powerful pattern recognition capability. However, due to the “closed-box” nature, deep-learning methods have long been blamed for their poor interpretability when modeling a physical system. In this article, we introduce a novel neural network structure, Kolmogorov–Arnold network (KAN), to achieve “glass-box” modeling for electrical energy systems to enhance the interpretability. The most distinct feature of KAN lies in the learnable activation function together with the sparse training and symbolification process. Consequently, KAN can express the physical process with concise and explicit mathematical formulae while retaining the nonlinear-fitting capability of deep neural networks. Simulation results based on three electrical energy systems demonstrate the effectiveness of KAN in the aspects of interpretability, accuracy, robustness, and generalization ability.