XGBoost Reaches R² of 0.980 for EV Charging Demand, Ahead of Transformer, in Scientific Reports Study of 55 Stations
Scientific Reports published a study on electric vehicle charging demand forecasting on November 13, 2026. The study developed an explainable multi-modal forecasting approach fusing charging session data, weather information, grid prices, grid operating information, station occupancy and traffic signal status. Using 15,304 charging session records from 55 charging stations between January 2019 and July 2020, the author constructed a 13,680-hour multi-modal data panel and evaluated five forecasting architectures: linear regression, Random Forest, XGBoost, a Long Short-Term Memory network and a proposed attention-based Transformer network. XGBoost achieved the lowest RMSE and highest R², at RMSE of 5.63 kWh and R² of 0.980, while Random Forest achieved a marginally lower MAE and MAPE, at 1.67 kWh and 7.39 percent, versus XGBoost's 1.72 kWh and 8.36 percent. Both tree-based models substantially outperformed the Transformer network, at RMSE of 27.98 kWh and R² of 0.513, and the LSTM network, at RMSE of 30.02 kWh and R² of 0.440. SHAP analysis of the best model identified recent past charging demand, transformer loading and the hour of the day as the most significant predictors. The paper, by Md Mushfiq Us Salehin, carries DOI 10.1038/s41598-026-78819-1.
(Source: Scientific)
Source: Scientific
Published by GSM · USA (original Chinese edition)
Photo: 图：一处电动车充电站与正在充电的车辆。Julian Herzog (Website)，CC BY 4.0，经 Wikimedia Commons。 / Photo: Julian Herzog (Website), CC BY 4.0, via Wikimedia Commons.
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