Electric Vehicle Charging Load Forecasting: An Experimental Comparison of Machine Learning Methods

TL;DR

This paper systematically compares five forecasting models, including machine learning and statistical methods, for predicting electric vehicle charging demand across various timeframes and spatial scales using real-world data.

Contribution

It is the first study to evaluate EV charging load forecasting across diverse temporal horizons and spatial levels with multiple datasets.

Findings

Machine learning models outperform traditional methods in short-term forecasts.

Forecast accuracy varies significantly across different spatial scales.

Deep learning methods show promise for long-term demand prediction.

Abstract

With the growing popularity of electric vehicles as a means of addressing climate change, concerns have emerged regarding their impact on electric grid management. As a result, predicting EV charging demand has become a timely and important research problem. While substantial research has addressed energy load forecasting in transportation, relatively few studies systematically compare multiple forecasting methods across different temporal horizons and spatial aggregation levels in diverse urban settings. This work investigates the effectiveness of five time series forecasting models, ranging from traditional statistical approaches to machine learning and deep learning methods. Forecasting performance is evaluated for short-, mid-, and long-term horizons (on the order of minutes, hours, and days, respectively), and across spatial scales ranging from individual charging stations to regional and city-level aggregations. The analysis is conducted on four publicly available real-world datasets, with results reported independently for each dataset. To the best of our knowledge, this is the first work to systematically evaluate EV charging demand forecasting across such a wide range of temporal horizons and spatial aggregation levels using multiple real-world datasets.