Developing a Multi-Region Stacking Ensemble Framework via Scenario-Based Digital Twin Simulation for Short-Term Household Energy Demand Forecasting

Modern energy grids, with their regional diversity and complex consumption patterns, require accurate short-term forecasting for operational efficiency and reliability. This study introduces a Stacking Ensemble Forecasting (SEF) framework for multi-region household energy demand, utilizing an optimized stacking ensemble model tuned via Bayesian Optimization to achieve superior predictive accuracy. The framework significantly improved accuracy across Diyarbakır, Istanbul, and Odemis, with a final model demonstrating up to 16.47% RMSE reduction compared to the best baseline models. The final model’s real-world performance was validated through a Simulated Digital Twin (SDT) environment, where scenario-based testing demonstrated its robustness against behavioral changes, data quality issues, and device failures. The proposed SEF-SDT framework offers a generalizable solution for managing diverse regions and consumption profiles, contributing to efficient and sustainable energy management.