Rural agricultural regions such as the Chalan Beel area in Bangladesh often suffer from unreliable grid access, high diesel dependence, and the absence of predictive energy management. This paper presents an optimized islanded DC hybrid renewable energy system integrating photovoltaic (PV), proton exchange membrane fuel cell (PEMFC), and battery energy storage system (BESS) for rural agricultural electrification. A high-resolution, three-year hourly load dataset is developed to represent multi-sectoral demand, including residential, irrigation, agro-processing, and aquaculture loads, for a representative region in Bangladesh. The primary contribution of this work lies in the integration of realistic agricultural load modeling, predictive LSTM-assisted dispatch scheduling, dynamic DC microgrid control, and techno-economic optimization into a unified framework for rural agricultural electrification. In addition, a voltage-sensor-based maximum power point tracking (MPPT) technique and an adaptive fuzzy-PID controller are implemented to improve dynamic stability while reducing control hardware complexity. The proposed system was developed and validated through a comprehensive software-based simulation framework using MATLAB/Simulink and HOMER Pro. Comparative analysis with conventional rule-based and non-predictive control strategies demonstrates that the proposed LSTM-assisted framework reduces hydrogen consumption by 18%–24% and improves dispatch accuracy (R 2 = 0. 979) under varying seasonal conditions. Techno-economic optimization indicates a levelized cost of energy (LCOE) of 0. 145/kWh and a net present cost (NPC) of 245, 000 over a 20-year project lifetime. Sensitivity analysis further confirms the robustness of the proposed system against variations in hydrogen price, solar irradiance, and load demand. Compared to diesel-based and PV-diesel hybrid systems, the proposed architecture achieves up to 87% reduction in carbon emissions while maintaining superior reliability (99. 2%). Overall, the results demonstrate that integrating predictive intelligence with hybrid energy systems significantly enhances economic viability and operational efficiency for off-grid agricultural communities.
Esha et al. (Mon,) studied this question.