Rim Mbarki completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-free gas-turbine TEY predictor end to end: selected only the eight available sensor inputs, excluded downstream CO/NOx emissions, and validated on a locked-away later time period. Compared a LinearRegression baseline with a RandomForest, reported held-out MAE/RMSE/R², inspected residuals for bias, and documented deployment limits plus a concrete drift-monitoring plan.

• Leakage-Free Feature Selection
• Time-Based Model Validation
• Honest Error Analysis
• Operational Monitoring Design