An Intelligent Machine Learning Based Load Shedding Prediction and Smart energy Management Framework for power grid Optimization in Bangladesh

Bangladesh's power sector endures chronic load shedding attributable to a widening demandsupply imbalance, compounded by the inherently reactive disposition of conventional grid management systems. This study presents an intelligent, data-driven framework that integrates machine learning and time-series modelling to predict load shedding occurrences, forecast electricity demand and facilitate optimized smart energy allocation. Utilizing real-world power system data, Random Forest and XGBoost classifiers are employed for load shedding prediction, while Prophet and Long Short- Term Memory (LSTM) networks furnish multi-horizon demand forecasting. SHAP analysis is incorporated to ensure rigorous model interpretability and operational transparency. Results demonstrate high predictive accuracy, a pronounced correlation between ambient temperature and electricity demand and a substantive reduction in unnecessary load shedding through intelligent allocation strategies. This work contributes a scalable and interpretable smart grid framework specifically tailored to the infrastructural exigencies of Bangladesh and analogous developing economies.