Interpretable Short-Term Electricity Demand Forecasting for the PGCB Grid Using Feature Fusion

The main contribution of this study is a feature-engineered short-term electricity demand forecasting framework that integrates historical demand, temporal/calendar, and weather information, combines rigorous leakage-aware chronological evaluation with machine learning optimization, and provides interpretable predictions using SHAP analysis for the PGCB grid.