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.

In this paper, we propose a novel reinforcement learning (RL)-aided adaptive scheduling mechanism for fair resource scheduling of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) traffic in 5G networks. To this end, we introduce a comprehensive Simulink simulation tool that visually facilitates intelligent QoS management based on adaptive thresholds and feedback performance loops. The suggested approach dynamically allocates resources by designing RL-like adaptive learning logic using off-the- shelf Simulink blocks. It also adapts in real-time to evolving traffic conditions by providing quality-of-service (QoS) differentiation. Experimental results demonstrate that our method achieves a 67% improvement in system efficiency and a 45% reduction in QoS violations compared to conventional baselines. This reflects effective learning dynamics and improved resource utilization, with O(1) computational complexity per scheduling decision.