Real Data · 90-Day Backtest

Backtest Results: Real Market Data

Deterministic DP · DAMagedOpt vs. operating baseline · 1 October 2024 – 29 December 2024

+19.90%

Net value uplift vs baseline

€0,44M

Net gain (90 days)

+52.96%

Gross revenue uplift

€-734.982

Wear cost avoided

46/90

Positive uplift days

0.06

Sharpe-like consistency

Monthly Net Value: DAMagedOpt vs Baseline

Net value = revenue − wear cost. All figures in EUR.

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Deterministic DP, SMARD DE real day-ahead prices, Open-Meteo inflow features

Methodology

Price source
SMARD DE real data (German DE-LU day-ahead)
Test period
1 October 2024 – 29 December 2024 (90 days)
Solver
Stochastic Dynamic Programming, rolling 8h horizon
Baseline
baseline_peak_hours: best-efficiency turbine during top-8 price hours per UTC day; standby otherwise.
Inflow model
Open-Meteo meteo → ML inflow forecaster (Grande Dixence)

Backtest uses real SMARD DE-LU day-ahead prices for 1 Oct–29 Dec 2024 and compares DAMagedOpt dispatch against the operating baseline described above. Past performance does not guarantee future results.

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What's inside the €49 report

  • Dispatch methodology: Dynamic Programming model, 36h rolling horizon, SMARD DE-LU day-ahead prices
  • 2024 DE-LU headline results: gross revenue uplift +19.95%, net value uplift +19.94% over Oct–Dec 2024
  • Month-by-month breakdown: optimised vs. baseline net value for Oct, Nov, and Dec 2024
  • Wear cost analysis: quantified avoided turbine wear cost per dispatch decision
  • Inflow model: Open-Meteo meteorological features → ML inflow forecaster (Grande Dixence proxy)
  • Caveats & limitations: data sources, model assumptions, and real-world applicability notes

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