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

arXiv: https://arxiv.org/abs/2405.19053 · HTML: https://mlsanity.com/paper/2405.19053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 3 of 3)

- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting](https://mlsanity.com/paper/2103.16349/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing](https://mlsanity.com/paper/2506.19860/) (2025)

## Abstract (arXiv)

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.