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

TL;DR

This paper introduces MIK-TST, a two-stage transfer learning framework combining Mixer, Informer, and KAN to improve long-sequence load forecasting for new EV charging stations, achieving significant accuracy improvements.

Contribution

The paper presents a novel two-stage transfer learning approach integrating Mixer, Informer, and KAN for accurate long-sequence load forecasting in newly constructed EV charging stations.

Findings

Achieved 4% reduction in MAE

Achieved 8% reduction in MSE

Outperformed baseline models

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.