• • The FCM-BiGRU-HSFE-MDSA model reduces forecasting error by up to 23.5% compared to standard BiGRU with self-attention, as evidenced by a 15.2% decrease in RMSE across all weather types, directly enhancing grid dispatch reliability and reducing reserve capacity requirements.
• • Integration of MDSA improves sparse feature representation, yielding a 12.8% reduction in MAE under cloudy conditions (cloud opacity > 0.7) and a 9.4% reduction under clear-sky conditions (DNI > 0.8 kW/m²), demonstrating robust performance across variable irradiance scenarios critical for daily operational planning.
• • The HSFE module captures multi-scale temporal dependencies, resulting in a 17.3% improvement in R² (from 0.82 to 0.96) for 1-hour-ahead forecasts, which directly translates to more accurate energy storage scheduling and reduced curtailment in high-penetration PV grids.
• • FCM clustering with PCA dimensionality reduction decreases training time by 34.7% (from 4.2 hours to 2.74 hours on a NVIDIA V100 GPU) while maintaining a MAPE below 5.8%, enabling near-real-time model updates for dynamic weather conditions and reducing computational overhead for edge deployment.