Provenance:Dataset:Turkey-Energy-v2024Model:PPO-v3.2 / ENSM-v4.0Run:27 Nov 2025rl_ppo_20251127_114523_r…Seeded

Key Results

Primary findings from the 2024–2050 energy transition optimisation. All results are from immutable experiment runs with seed=42. Statistical significance confirmed across 78 Diebold-Mariano tests and one-way ANOVA (p<0.001) for both forecasting and RL components.

Executive Summary: Turkey's 2050 energy transition optimisation achieves 97.34% renewable share at $226.79B total cost with 90.6% CO₂ reduction. PPO policy delivers the optimal cost pathway; fossil phase-out completes in 2041. All 13 RL algorithms exceed the 90% renewable target; statistical significance confirmed (ANOVA p<0.001).

Interpretation Guardrail

These results represent the optimal pathway under the specified model assumptions. Real-world outcomes depend on policy implementation, technology cost trajectories, and grid constraints not fully captured in the model.

97.34%

Renewable 2050

7.34 pp above 90% target

CI [88.7 – 97.34]

$226.79B

Total System Cost

90.6%

CO₂ Reduction

180.76GW

Capacity 2050

37.24 vs 218 GW target

2041

Fossil Phase-out

88.7%

MC Worst Case

1.30 100-run Monte Carlo floor

CI [88.7 – 97.34]

2050 Terminal Target Compliance

3/4 targets met

⚠️

Capacity 2050

≥ 218 GW

✅

Renewable Share

≥ 90%

✅

Emission Reduction

≥ 80% CO₂ cut

✅

Cost Range

$200–450B

Annual Investment vs $10B Cap

2025— $10B cap —2050

📈 Results & Analysis

Comprehensive analysis integrating Forecast (ENSM/HNSM), Reinforcement Learning (13 algorithms), 2050 target compliance, statistical validation, and economic/emission outcomes. Use the tabs below to explore each dimension.

☀️

97.34%

Renewable 2050

+7.34 pp vs target

💰

$226.79B

Total Cost

0.35% GDP/yr

🌍

90.6%

CO₂ Reduction

152→14.3 Mt

📈

0.9799

Forecast R²

ENSM vs 16 benchmarks

🤖

PPO

RL Winner

Best cost + stability

🎲

88.7%

MC Worst Case

100-run robustness

Integrated Framework Outcomes

Forecast Component

ENSM achieves R²=0.9799 on generation forecast, 35% improvement over LSTM. 5-fold CV, 78 Diebold-Mariano tests (p<0.001). Monte Carlo PICP=94.2%.

RL Component

13 algorithms compared. PPO optimal cost ($226.79B). SAC best reward (5271). All achieve >96% renewable. ANOVA F=87.34, p<0.001.

Policy Implications

97.34% renewable by 2050 feasible. Fossil phase-out 2041. Solar 32%, Wind 28%, Hydro 27%. $8.7B/yr average investment (0.35% GDP).

Transition Pathway & Visual Analysis

Optimal Technology Mix — 2050 (PPO Run 1)

Solar PV32%

58.12 GW

Wind28%

50.99 GW

Hydro27%

49.57 GW

Nuclear3%

4.80 GW

Other RE10%

17.28 GW

Fossil0%

0.00 GW

Pathway & Trade-Off Charts

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Key Milestones (PPO Optimal Policy)

The PPO policy achieves rapid renewable ramp-up from 60.79% (2025) to 97.34% (2050). The 2041 fossil phase-out event causes the notable step-change in CO₂ emissions. Full year-by-year data: RL Comparison →

Year	Total Capacity (GW)	Renewable Share	Annual Cost ($B)	CO₂ Emissions (Mt)	Milestone
2025	119.36	60.79%	$13.197	145.37	Baseline — grid expansion begins
2030	121.23	77.13%	$9.608	76.64	77% renewable achieved
2035	133.77	86.59%	$8.191	37.40	87% renewable — solar ramp
2040	149.80	91.56%	$8.015	30.57	92% renewable — cost inflection
2041	152.40	93.50%	$7.950	11.62	⚡ Fossil phase-out event — CO₂ step-change
2045	160.98	97.02%	$7.476	13.50	97% threshold crossed
2050	180.76	97.34%	$8.337	14.30	🎯 97.34% — target exceeded +7.34 pp

13-Algorithm 2050 Results (Run 1)

All algorithms were trained on TurkeyEnergyTransitionEnv (100 episodes, seed=42). Primary reporting uses Run 1 results. Run 2 data available at full RL comparison. ANOVA F=87.34, p<0.001, η²=0.82.

Algorithm	Category	Total GW	Renewable %	Cost ($B) ↑	CO₂ (Mt)	Solar (GW)	Wind (GW)	Hydro (GW)	Targets
TD3	rl	130.43	96.32%	$207.34	8.98	29.06	65.89	17.82	✅✅✅
MAML	meta-learning	170.52	97.19%	$219.83	13.55	54.90	47.77	46.34	✅✅✅
FuzzyQL	fuzzy	172.30	97.21%	$221.15	13.82	57.39	45.64	47.65	✅✅✅
NF	fuzzy	171.96	97.21%	$221.91	13.67	55.29	48.09	46.92	✅✅✅
PNN	meta-learning	171.99	97.21%	$221.95	13.66	55.34	48.21	46.78	✅✅✅
GA	evolutionary	176.42	97.28%	$223.80	14.10	57.89	47.88	48.57	✅✅✅
PPO	rl	180.76	97.34%	$226.79	14.30	58.12	50.99	49.57	✅✅✅
A3C	rl	220.17	97.82%	$227.43	19.15	70.76	19.80	117.14	✅✅✅
DE	evolutionary	172.67	97.22%	$227.61	13.17	38.21	56.31	56.44	✅✅✅
SAC	rl	198.18	97.58%	$230.70	14.88	85.04	77.91	17.82	✅✅✅
DQN	rl	207.72	97.69%	$242.97	15.65	79.61	90.41	17.83	✅✅✅
MARL	multi-agent	220.17	97.82%	$243.38	16.86	98.77	91.64	17.82	✅✅✅
PSO	evolutionary	215.66	97.77%	$246.38	16.80	71.68	60.33	60.75	✅✅✅

Click column headers to sort. ✅ indicators: Renewable ≥90% · Cost $200–450B · CO₂ ≥80% reduction. Nuclear (Akkuyu) fixed at 4.80 GW across all runs. Full dual-run data: RL Comparison →

Detailed Result Pages

📈

Forecast Results (ENSM / HNSM)

Full 17-model benchmark, ENSM R²=0.9799, S-curve parameters, GRU architecture, uncertainty quantification (PICP=94.2%), 5-fold cross-validation.

🤖

RL Algorithm Comparison

All 13 algorithms (2 runs each), year-by-year trajectories, technology portfolios, cost breakdowns, algorithm family analysis, ANOVA + Tukey HSD.

⚠️

Failure Modes & Limitations

Documented conditions where models underperform: RL instability at high RE shares, price spike forecast degradation, DQN discretisation limitations.

Key Figures

System Architecture — Hybrid AI Framework — Fig. 4.1 — Hybrid AI system architecture: ENSM forecasting → MAPPO optimization → Bidirectional integration.

17-Model Forecast Performance Ranking — Fig. 3.4 — All 17 forecasting models ranked by R². ENSM (0.9799) leads by a wide margin over LSTM (0.8932).

Performance Metrics Comparison — Fig. 3.6 — Multi-metric comparison (R², MAE, RMSE, MAPE) across all 17 models.

Evidence & Reproducibility

All experiment runs are immutable (isImmutable=true) with seeded random state (seed=42). Full run registry available at Data & Datasets.

Research Questions & Answers

Click to expand each question.

Yes. All 13 algorithms achieve >96% renewable share by 2050. The optimal pathway (PPO) achieves 97.34%, exceeding the 90% target by +7.34 percentage points.

PPO delivers the optimal cost-to-renewable trade-off at $226.79B total (0.35% GDP/yr). SAC achieves higher rewards but at greater variance across runs.

Yes. ENSM achieves R²=0.9799 — significantly outperforming LSTM (0.8932) and all 15 other benchmarks across 78 Diebold-Mariano tests (p<0.001).

Solar 32% (58.12 GW), Wind 28% (50.99 GW), Hydro 27% (49.57 GW), with Nuclear at 4.80 GW (Akkuyu) and complete fossil fuel phase-out by 2041.

100-run Monte Carlo validation shows worst-case renewable share of 88.7% (−8.64 pp), confirming robustness. All runs exceed the 80% threshold.

$226.79B total over 2024–2050 (~$8.7B/yr average), equivalent to 0.35% of GDP annually. Peak investment occurs in 2025 (~$13.2B) declining to $7.5B/yr by 2045.

📎 Cite this — Key Results

Önderol, S. (2025). Long-term renewable energy forecasting and multi-agent reinforcement learning optimization for Turkey's energy transition to 2050 (Doctoral dissertation). Erciyes University.

URL: https://renewabletransition.org/transition/results

Algorithm note: All 13 RL algorithms tested — PPO, DQN, SAC, A3C, TD3 (RL family), GA, PSO, DE (Evolutionary), FuzzyQL, Neuro-Fuzzy (Fuzzy), MARL/MAPPO (Multi-Agent), MAML, PNN (Meta-Learning). Seed=42 for all runs.