Self-directed project
Energy and Renewable: Machine Learning for the Power Grid
Junior Machine Learning Engineer · AI / ML · August 2026
A ProoV case study · educational project, not employment
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.
Graded against
- Build and validate the model correctly35%
- Optimize and evaluate honestly25%
- Write the Model Card (clarity + honesty)25%
- Explain it to a non-expert15%
Passed · pass mark 60/100
What stood out6
- 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
The work I submitted20 tasks
12 tasks · 13 lines · Python, JavaScript · 8 written answers · 431 words
- Problem Frame227 characters
- Leakage Flags113 characters
- Train Validate BaselinePython · 1.4k characters
- Train Validate Forest943 characters
- Train Validate124 characters
- Tune Evaluate SetupPython · 1.5k characters
- Tune Evaluate ResidualsPython · 2 lines
- Tune Evaluate130 characters
- Residual Read351 characters
- Drift Monitor91 characters
- Model Card1.5k characters
- Teach BackJavaScript · 719 characters
- Problem Frame22 words
- Leakage Flags1 word
- Train Validate Baseline140 words
- Train Validate Forest76 words
- Train Validate1 word
- Tune Evaluate Setup110 words
- Tune Evaluate Residuals80 words
- Tune Evaluate1 word
Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.