Damian Englot completed Energy and Renewable: Machine Learning for the Power Grid

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 tuned RandomForest, and validated on a chronological holdout. The final model achieved 1.53 MWh MAE, 1.937 RMSE, and 0.983 R² on unseen later-period data, with a model card that clearly states intended use, limits, and monitoring triggers.

• Leakage-Free Feature Selection
• Time-Based Holdout Validation
• Honest Model Comparison
• Plain-Language Model Communication