Intelligent Energy Management of Hybrid Microgrid Using Deep Learning–Based Optimal Predictive Controller

The increasing integration of renewable energy sources into hybrid microgrids has significantly improved the sustainability of power systems. However, the intermittent nature of solar and wind generation makes real‐time energy management a major challenge. Conventional energy management strategies often exhibit limited adaptability to rapidly changing operating conditions, resulting in inefficient power dispatch, increased fuel consumption, and accelerated battery degradation. To address these limitations, this study proposes an intelligent energy management framework based on the integration of a gated recurrent unit (GRU) deep learning model with model predictive control (MPC) for a grid‐connected hybrid microgrid supplying residential buildings in Maroua, Cameroon. The GRU model predicts load demand and renewable energy generation using historical and real‐time data, while the MPC optimally schedules power flows by considering system constraints, battery state of charge, renewable energy availability, and load requirements. The proposed GRU–MPC strategy was implemented and validated in MATLAB/Simulink using real operational data from the study area. Simulation results demonstrate that the proposed approach achieves a 30% reduction in fuel consumption, a 15% improvement in overall system efficiency, a 20% reduction in battery stress, and a forecasting accuracy of 99.8% compared with conventional energy management approaches. In addition, the proposed framework improves renewable energy utilization, maintains the battery state of charge around 65%, ensures continuous power supply, and reduces the operating energy cost to approximately 25 USD under the investigated scenarios. These results confirm that the GRU–MPC strategy provides an effective, robust, and scalable solution for intelligent energy management in modern hybrid renewable energy microgrids.