Namratha Chidireddi 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 evaluated on a locked-away later-year test split. The final model achieved 1.50 MWh MAE, 1.908 RMSE, and 0.9838 R² on held-out data, with a model card that clearly states limits, drift risks, and monitoring triggers.

• Leakage-Free Feature Selection
• Honest Held-Out Evaluation
• Model Risk Communication