An Integrated Framework for Forecast-Driven Priority-Aware Energy Allocation in Microgrids

Abstract

Managing multi-building smart grids requires accurate demand forecasting, efficient resource allocation, and robust real-time control under uncertainty. This paper presents an integrated energy management framework that combines deep learning forecasting, metaheuristic optimization, and Model Predictive Control (MPC) for priority-aware energy reallocation. A hybrid CNN-LSTM model captures multivariate temporal dependencies to deliver short-term demand predictions. To maximize accuracy and eliminate manual tuning, a Grey Wolf Optimizer (GWO) fine-tunes the network’s hyperparameters and training configurations. These optimized forecasts serve as inputs to a receding-horizon MPC module, which determines cost-effective dispatch decisions that minimize operating costs and peak grid demand while enforcing building-level priority constraints and battery operational limits. Finally, a sequential sensitivity analysis identifies the optimal operational knee point to balance the trade-offs among peak shaving, economic cost, non-critical load tracking, and battery cycling. By coupling optimized learning-based prediction with closed-loop decision-making, the proposed modular and scalable architecture enables resilient microgrid energy management.