Parth Budhale 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, 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.

• Leakage-Free Feature Selection
• Time-Based Holdout Evaluation
• Honest Model Comparison
• Basic Drift Monitoring Design