Muhammad Ashraf Bin Tajuddin 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 time-based train/test split, and honest held-out evaluation. Compared LinearRegression against a RandomForestRegressor, then documented the model’s accuracy, limitations, and monitoring plan in a concise model card. The final system predicts hourly turbine energy yield in MWh and is positioned for plant-operator use, not cross-plant generalization.

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