• • The proposed Stacking ensemble model reduces mean absolute percentage error (MAPE) by 18.7% compared to XGBoost alone and by 12.3% compared to the best base learner (CNN-BiLSTM-MultiHeadAttention) on the southern China dataset, with MAPE values of 1.92% versus 2.36% and 2.19%, respectively, demonstrating industrial-grade accuracy for economic dispatch.
• • Spatiotemporal joint rolling sampling improves forecasting accuracy by 9.4% over traditional time-series sampling, as evidenced by a reduction in root mean square error (RMSE) from 156.3 MW to 141.6 MW, enabling better utilization of cross-zone correlations and reducing reliance on stale historical data.
• • BOHB hyperparameter optimization achieves a 7.2% reduction in validation loss compared to random search, with convergence within 50 iterations, and the optimized XGBoost base learner attains an R² of 0.963, underscoring the efficacy of automated tuning for heterogeneous models.
• • The model exhibits superior robustness during step-load events, with a maximum absolute error (MAE) of 3.45% during rapid load changes, compared to 5.12% for LSTM, and maintains stable performance under non-stationary conditions, as indicated by a 22.1% lower standard deviation of errors across 24-hour rolling forecasts.