Self-directed project
Energy and Renewable: Machine Learning for the Power Grid
Junior Machine Learning Engineer · AI / ML · July 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, compared a LinearRegression baseline with a tuned RandomForest, and evaluated on a locked later-period holdout. Reported honest held-out performance (about 1.5 MWh MAE, 1.94 RMSE, R2≈0.983) and documented limitations, drift risks, and a retraining trigger in a concise model card.
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 emissions to avoid leakage
- Used an earlier-years train / later-years test split instead of a random shuffle
- Reported held-out MAE, RMSE, and R2 for the tuned model
- Included a concrete monitoring trigger tied to rolling error and input distribution shift
- Leakage-Free Feature Selection
- Time-Based Holdout Evaluation
The work I submitted19 tasks
11 tasks · 12 lines · Python · 8 written answers · 436 words
- Problem Frame175 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 Evaluate132 characters
- Residual Read237 characters
- Drift Monitor91 characters
- Model Card1.0k characters
- Problem Frame14 words
- Leakage Flags1 word
- Train Validate Baseline141 words
- Train Validate Forest76 words
- Train Validate1 word
- Tune Evaluate Setup110 words
- Tune Evaluate Residuals92 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.