2️⃣2️⃣2️⃣Deep Learning-Based Predictive Energy Management System for Smart Microgrids
🔴Abstract Accurate forecasting of renewable generation and electrical demand has become an essential component of modern energy management systems. Traditional rule-based EMSs are incapable of exploiting future operating information, leading to suboptimal scheduling decisions and increased operating costs. This research proposes an intelligent predictive energy management framework that integrates deep learning forecasting models with adaptive optimization techniques for smart microgrids. Long Short-Term Memory (LSTM) and Transformer neural networks are employed to predict photovoltaic generation and load demand several hours ahead. These forecasts are then incorporated into an adaptive optimization engine that determines the optimal power dispatch strategy among renewable generators, battery storage systems, and the utility grid. The proposed framework minimizes energy costs, battery degradation, and renewable energy curtailment while maximizing system reliability under uncertain operating conditions. The expected outcomes include superior forecasting accuracy, enhanced energy utilization, reduced operating expenses, and improved operational flexibility compared with conventional predictive and rule-based energy management approaches.
August 4, 2026 204 4
