Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting

Reliable short-term electric load forecasting (STLF) is essential for enhancing grid stability, optimizing energy distribution, and minimizing operational costs in modern power systems. However, existing forecasting models, including statistical approaches and deep learning architectures such as multi-layer perceptron (MLP), struggle to capture complex nonlinear load variations while maintaining computational efficiency. To overcome these limitations, a self-adaptive Kolmogorov-Arnold network (SADE-KAN), an optimized forecasting framework that combines the power of Kolmogorov-Arnold networks (KAN) with self-adaptive differential evolution (SADE) is introduced to enhance both predictive accuracy and computational efficiency. Unlike conventional MLP models, KAN replaces fixed activation functions with spline-based learnable functions that offers greater flexibility in capturing temporal dependencies. However, these learnable activation functions introduce a new set of hyperparameters that require careful optimization to ensure efficient training and manage network complexity. To address this, SADE dynamically tunes these hyperparameters, ensuring an optimal balance between accuracy, complexity, and training efficiency. The proposed SADE-KAN model is validated on ISO-NE hourly load data (2019-2023, ~ 1 million observations) across multiple forecasting horizons (24, 48, 96, and 168 h). Experimental results demonstrate that SADE-KAN reduces mean absolute percentage error (MAPE) by up to 35% and root mean squared error (RMSE) by 38% compared to MLP models, while requiring 35% fewer learnable parameters. Despite a slightly higher training time, SADE-KAN significantly enhances generalization and robustness, capturing rapid load fluctuations more effectively than MLP, conventional KAN and other recently published advanced models. These findings establish SADE-KAN as a computationally efficient and highly accurate forecasting framework, offering a robust solution for real-time power system applications, demand response strategies, and energy market operations.