Performance testing and validation of integrated systems

The analysis considers the interaction between RL and the unidirectional forecast. Convergence is analysed in Section 4.3.1, uncertainty propagation in Section 4.3.2, robustness across scenarios in Section 4.3.3 and benchmarks validation in Section 4.3.4. In 4 iterations, integration converges and forecast-policy gap declines from 8.2 to 0.9 GW, for a −89% result. Cost fluctuation will be limited to ±10B € when Monte Carlo ensembles will be used. The pathway is compatible with IEA Net Zero 2050 and IRENA 1.5°C while considering Turkey-specific constraints.

The dynamics of convergence in forecast-RL

As RL rollouts refine forecasts, bidirectional integration constantly retrains RL with updated predictions. Convergence metric: average absolute capacity forecast gap with reinforcement learning optimized across all technologies and years.

Convergence path (4 loops)

: Forecast-RL Integration Convergence Dynamics: Iterative Refinement and Gap Reduction {#tbl:forecast-rl-convergence-dynamics}

Main insights

1. Rapid initial convergence: gap -43% (8.23→4.67 GW) after Iteration 1. Non-integrated ENSM under-forecasts solar by 12 GW (56 vs 68 GW 2040–2050); Iteration 1 lifts forecast to 64 GW (7% error cut).
2. Diminishing returns: 50% (1→2), 48% (2→3), 25% (3→4); Iteration 4 gap 0.91 GW (~0.4% of 218 GW), negligible.
3. Forecast accuracy: ENSM MAE 1.5→0.87 GW (−42%) as learning curves/subsidies/retirements enter training via RL data.
4. Policy return: -0.142→-0.054 (+62%) using stochastic forecasts (optimistic/baseline/pessimistic).
5. Compute: 4 iters = 65.6h vs. 16.4h non-integrated; 4× compute for ~33% forecast and 62% policy gain over 26-year horizon.

Evaluation of convergence criterion: Iteration 4 fulfills that (1/N) * summation |K(4)forecast-K(4)RL| = 0.91 GW < ε= 2. Additional iterations (5-7) trim <0.1 GW gap & <1% return; costly, not justified. Iteration 4 satisfies $\frac{1}{N}\sum |K_{forecast}^{(4)}-K_{RL}^{(4)}|=0.91$ GW < ε = 2 GW. Extra iterations (5-7) cut <0.1 GW gap and <1% return, not cost-justified.

Convergence Inherent to Technology

: Technology-Specific Convergence: Forecast-Policy Gap Reduction by Technology {#tbl:technology-convergence-gap}

Solar has the largest initial gap (11.8 GW) because historical growth (2015-2023: 3.2 GW/year) misses policy-driven acceleration (2035-2040: 4.1 GW/year). Integration captures this. Hydro gap is small (0.4 GW) due to maturity/limited expansion; nuclear gap is zero (fixed Akkuyu schedule).

Forecast-RL integration convergence: (a) Forecast-policy gap (GW) versus iterations: shows an exponential decay in forecast-policy gap, from 8.23 GW (iteration 0) to 0.91 GW (iteration 4), or 89% reduction. (b) Forecast accuracy (R squared) versus iterations: Forecast accuracy in terms of R squared improves from 0.91 (iteration 0) to 0.94 (iteration 4). (c) RL performance (renewable share %) versus iterations: RL performance stabilizes at 97.34% at iteration 4. (d) Convergence criteria (fixed-point iteration): gap reduction rate varies from 43-50% per iteration and gap convergence threshold at ε=2 GW. Track iterative performance across all forecasts, policies, and RL performance at every iteration. According to the new results, using the forecast values as input by multiple sources enhances accuracy and performance at every iteration for convergence.

[Figure]

*Forecast-RL integration convergence: (a) Forecast-policy gap (GW) vs. iterations showing exponential decay from 8.23 GW (iteration 0) to 0.91 GW (iteration 4), 89% reduction. (b) Forecast accuracy (R²) vs. iterations improving from 0.91 (iteration 0) to 0.94 (iteration 4). (c) RL performance (renewable share %) vs. iterations stabilizing at 97.34% (iteration 4). (d) Convergence criteria (fixed-point iteration) showing gap reduction rate (43-50% per iteration) and convergence threshold (ε=2 GW).*

Comparison of the ENSM forecast and the RL MAPPO prediction of ASR C Module

: Comparison of Forecast and Reinforcement Learning Results for 2050 {#tbl:comparison-of-forecast-vs-rl-results-2050}

The forecasting and RL results were close with capacity difference being 0.57% (1.24 GW) and generation difference being 0.12% (0.85 TWh). The renewable share in the RL optimization is higher (97.34% vs 95.2%) compared to the forecast solution. The wind capacity in the RL optimization is also higher (84.68 GW vs 78.5 GW). The difference of CO₂ (14.30 Mt vs 8.4 Mt) is due to increased biomass and geothermal.

*Note: Source: Forecast: `advanced_neural_physical_2024_2050_20251017_231638.csv` (ENSM model, 2050), RL: `PPO_yearly_portfolio.csv` (2050). Forecast and RL results are close: capacity difference 0.57% (1.24 GW), generation difference 0.12% (0.85 TWh). RL optimization yields a higher renewable share (97.34% vs 95.2%) and higher wind capacity (84.68 GW vs 78.5 GW). The CO₂ difference (14.30 Mt vs 8.4 Mt) stems from increased biomass and geothermal.*

: The Performance of the Model and Algorithm {#tbl:model-performance-vs-algorithm-performance}

*Note: Source: Forecast: `comprehensive_results_20251017_231638.json` (ENSM), RL: `2050_final_portfolio.csv` (MAPPO). We assess the accuracy of the forecast (R²=0.9131, MAE=1.50 GW) and the success of RL optimization (97.34% renewable, 89.1% emission reduction). The two systems work together; bidirectional integration shortens forecast-policy gap from 8.23 GW to 0.91 GW (89% improvement).*

Uncertainty Propagation and Robust Optimization