Moksha S gowda completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end gas-turbine TEY predictor using leakage-free sensor inputs, a LinearRegression baseline, and a tuned RandomForest evaluated on a locked-away later-year test split. Reported honest held-out performance (about 1.5 MWh MAE, 1.93 RMSE, R2 0.983), documented why CO/NOx were excluded, and wrote a model card covering intended use, limits, and drift monitoring.

• Leakage-Free Feature Selection
• Time-Based Holdout Validation
• Honest Held-Out Evaluation
• Model Card Risk Communication