Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations

TL;DR

This paper introduces MSTEM, a multiscale spatio-temporal model leveraging graph neural networks and recurrent learning to improve short-term load forecasting accuracy at electric vehicle charging stations.

Contribution

The paper presents a novel multiscale graph neural network-based model that effectively captures hierarchical temporal dependencies and spatial interactions in EV charging load forecasting.

Findings

MSTEM outperforms six baseline models in accuracy.

The model effectively captures nonlinear temporal dependencies.

Real-world case studies validate its superior forecasting performance.

Abstract

The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. To address these challenges, we propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM incorporates a multiscale graph neural network to discern hierarchical nonlinear temporal dependencies across various time scales. Besides, it also integrates a recurrent learning component and a residual fusion mechanism, enhancing its capability to accurately capture spatial and temporal variations in charging patterns. The effectiveness of the proposed MSTEM has been validated through comparative analysis with six baseline models using three evaluation metrics. The case studies utilize real-world datasets for both fast and slow charging loads at EVCS in Perth, UK. The experimental results demonstrate the superiority of MSTEM in short-term continuous load forecasting for EVCS.