Parth Malasne 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 RandomForest model, and validated on a later held-out period. The final model achieved 1.53 MWh MAE, 1.94 RMSE, and 0.983 R² on held-out data, with a model card that clearly states intended use, limits, and monitoring triggers.

• Leakage-Free Feature Selection
• Time-Based Holdout Evaluation
• Honest Error Interpretation
• Model Card Communication