A Novel Predictive Control Approach Based on Hybrid Deep Learning Algorithm for Photovoltaic Power Forecasting in Microgrid

Abstract

With the increasing penetration of photovoltaic (PV) installations and their inherent intermittency due to weather variability, advanced energy control strategies have become essential for modern microgrid operation. In this context, reliable short‐term forecasting and optimal decision‐making are critical to ensure stable and efficient energy management. This work proposes a predictive energy management framework based on a parallel deep learning architecture combining long short‐term memory (LSTM) and gated recurrent unit (GRU) networks, whose hyperparameters are optimized using a multiobjective particle swarm optimization (MOPSO) algorithm for accurate PV power forecasting. The developed forecasting model is explicitly integrated into a model predictive control (MPC) scheme for real‐time microgrid energy management. In this MPC framework, the LSTM–GRU–MOPSO model provides multi‐step‐ahead PV power predictions that serve as key inputs to the optimization problem solved at each control interval (5 min). The MPC then determines optimal power dispatch decisions, including energy storage charging/discharging and grid interaction, while respecting system constraints and minimizing operational cost and power imbalance. The proposed approach is validated using a real PV dataset collected in Maroua, Far North Cameroon, under daily, weekly, and monthly prediction horizons. Results demonstrate that the hybrid forecasting model achieves high accuracy across daily, weekly, and monthly horizons, with R 2 values of 0.9984, 0.9988, and 0.9934, and RMSE values of 0.2096, 0.2034, and 0.3501, respectively. Comparative analysis shows that the proposed LSTM–GRU–MOPSO model outperforms conventional methods such as LSTM, GRU, support vector machine (SVM), and feedforward neural networks (FFNN), as well as hybrid architectures including LSTM–CNN and LSTM–RNN. The integration with MPC further enhances system reliability by enabling predictive, constraint‐aware, and adaptive microgrid energy management.