Selbstgesteuertes Projekt
Energy and Renewable: Machine Learning for the Power Grid
Junior Machine Learning Engineer · KI / ML · August 2026
Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis
Built a leakage-aware turbine yield predictor end to end: framed TEY as the target, excluded downstream CO/NOx emissions, trained a LinearRegression baseline and a RandomForest model, and validated on a genuine time-based holdout. The final model was evaluated honestly with MAE, RMSE, R², and residual analysis, and the accompanying model card documented intended use, limits, and monitoring triggers in plain language.
Bewertet nach
- Build and validate the model correctly35%
- Optimize and evaluate honestly25%
- Write the Model Card (clarity + honesty)25%
- Explain it to a non-expert15%
Bestanden · Bestehensgrenze 60/100
Was herausstach6
- Correctly excluded CO and NOx as downstream emission measurements to avoid leakage
- Used an earlier-year/later-year split instead of a random shuffle for validation
- Compared a LinearRegression baseline against a RandomForest and showed a clear held-out MAE improvement
- Read residual behavior and explicitly noted the model is less reliable at high load and outside observed operating conditions
- Leakage-Free Feature Selection
- Time-Based Holdout Validation
Meine eingereichte Arbeit20 Aufgaben
12 Aufgaben · 13 Zeilen · Python, JavaScript · 8 Textantworten · 431 Wörter
- Problem Frame227 Zeichen
- Leakage Flags113 Zeichen
- Train Validate BaselinePython · 1.4k Zeichen
- Train Validate Forest943 Zeichen
- Train Validate124 Zeichen
- Tune Evaluate SetupPython · 1.5k Zeichen
- Tune Evaluate ResidualsPython · 2 Zeilen
- Tune Evaluate130 Zeichen
- Residual Read351 Zeichen
- Drift Monitor91 Zeichen
- Model Card1.5k Zeichen
- Teach BackJavaScript · 719 Zeichen
- Problem Frame22 Wörter
- Leakage Flags1 Wort
- Train Validate Baseline140 Wörter
- Train Validate Forest76 Wörter
- Train Validate1 Wort
- Tune Evaluate Setup110 Wörter
- Tune Evaluate Residuals80 Wörter
- Tune Evaluate1 Wort
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