Meta‐Optimized Ensemble Learning With Gradient Boosting for Electric Vehicle Charging Demand Forecasting

The global transition toward electric vehicles (EVs) necessitates precise forecasting of charging demand to ensure grid stability and optimize infrastructure. Although gradient boosting (GB) Machines demonstrate significant potential for this task, performance limitations often arise from inadequate hyperparameter tuning. However, existing studies lack a systematic comparison of advanced metaheuristic hyperparameter optimization techniques and stacked ensemble learning under a unified forecasting framework for EV charging demand prediction. This study introduces a meta‐optimized forecasting framework that integrates advanced hyperparameter optimization techniques with a novel stacked ensemble architecture. A comprehensive comparison of five optimization methods, covariance matrix adaptation evolution strategy (CMA‐ES), Bayesian optimization (Hyperopt), particle swarm optimization (PSO), Optuna, and Randomized Search, is conducted to tune a GB model. Analysis indicates that CMA‐ES yields superior performance among single models, achieving a reduction in mean absolute error (MAE) by 23% compared to default parameters. Furthermore, the proposed stacked ensemble model amalgamates predictions from diversely tuned base learners through a meta‐learner, resulting in a breakthrough MAE of 0.94 kWh (a 53.6% reduction) and an accuracy measure of R 2 value of 0.95. This ensemble effectively eliminates extreme errors and exhibits exceptional temporal fidelity, demonstrating responsiveness to demand spikes with minimal lag. The findings offer clear guidelines for selecting optimization strategies based on accuracy–computational cost trade‐offs, establishing that systematic hyperparameter optimization in conjunction with ensemble learning represents a transformative approach for EV charging demand forecasting.