Vaishnavi Chougule completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end gas-turbine TEY predictor using leakage-free sensor features, with CO and NOx excluded because they are downstream emissions. Trained and compared a LinearRegression baseline and a RandomForest on a time-based held-out split, then evaluated the winner honestly with MAE, R2, and a residual read. The final model achieved about 1.53 MWh MAE and 0.983 R2 on unseen later-period data, with a clear model card describing intended use, limits, and drift monitoring.

• Leakage-Free Feature Selection
• Time-Based Holdout Validation
• Honest Model Comparison
• Residual Pattern Reading