Forecasting Results

# RESULTS AND ANALYSIS This chapter presents the results of the hybrid AI framework for forecasting, optimization, and integration system effectiveness. Section 4.1 presents the results of physics-informed neural forecasting and highlights ENSM's superior performance, with R²=0.9799 for generation, R²=0.9131 for capacity, MAE=1.5 GW for capacity, PICP=95.4%, MPIW=11.8 GW, compared to 17 benchmark methods with detailed statistical validation. Section 4.2 presents the reinforcement learning optimization results, demonstrating successful achievement of Turkey's 2050 targets with 97.34% renewable share, 14.30 MtCO₂ emissions, 90.6% emission reduction, $226.79B investment, and 220 GW capacity. This section further presents portfolio evolution and regional analyses. Section 4.3 examines the bidirectional integration of forecasting and reinforcement learning (RL). We quantify the algorithm's dynamic convergence, uncertainty propagation effects, and robustness through multiple examples. The results were validated through sensitivity analyses, cross-validation and comparison with IEA Net Zero 2050, IRENA 1.5°C pathways and earlier energy transition studies. All computational experiments were conducted on infrastructure equipped with 4× NVIDIA RTX 3090 GPUs (24GB VRAM each), 2× AMD EPYC 7763 CPUs (64 cores each), 512GB RAM, and 10TB NVMe storage. The system's total training time consists of 8.2 hours for ENSM, 8.4 hours for MAPPO, and 64 hours for the integrated system across four iterations. Results can be reproduced using the code repository (available upon request), Docker containers (with version tags), and data version control (DVC-tracked datasets).