Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations

Zhenhua Zhou, Bozhen Jiang, Qin Wang

2025-05-10Transfer Learning Load Forecasting

Abstract

The rapid rise in electric vehicle (EV) adoption demands precise charging station load forecasting, challenged by long-sequence temporal dependencies and limited data in new facilities. This study proposes MIK-TST, a novel two-stage transfer learning framework integrating Mixer, Informer, and Kolmogorov-Arnold Networks (KAN). The Mixer fuses multi-source features, Informer captures long-range dependencies via ProbSparse attention, and KAN enhances nonlinear modeling with learnable activation functions. Pre-trained on extensive data and fine-tuned on limited target data, MIK-TST achieves 4% and 8% reductions in MAE and MSE, respectively, outperforming baselines on a dataset of 26 charging stations in Boulder, USA. This scalable solution enhances smart grid efficiency and supports sustainable EV infrastructure expansion.

Related Papers

RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction2025-07-18 Disentangling coincident cell events using deep transfer learning and compressive sensing2025-07-17 Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows2025-07-16 Robust-Multi-Task Gradient Boosting2025-07-15 Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift2025-07-12 The Bayesian Approach to Continual Learning: An Overview2025-07-11 Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol2025-07-08 Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving2025-07-08