An integrated LSTM-MPA framework for intelligent hybrid renewable energy forecasting and charging optimization of campus electric bicycle and motorcycle systems
The increasing adoption of electric bicycles and motorcycles has intensified the demand for sustainable and reliable charging infrastructures, particularly in campus environments characterized by fluctuating mobility patterns and renewable energy variability. This study proposes an intelligent hybrid solar–wind renewable charging framework integrated with Long Short-Term Memory–Marine Predators Algorithm (LSTM-MPA) optimization to improve adaptive forecasting, charging stability, and renewable energy utilization. The methodology combined systematic meta-analysis and deep learning simulation approaches. A total of 30 empirical studies were analyzed using PRISMA-based selection procedures, risk-of-bias assessment, effect-size evaluation, and publication bias analysis. Experimental renewable energy datasets consisting of photovoltaic (PV), wind turbine, and charging parameters were modeled using the proposed hybrid LSTM-MPA architecture. The results demonstrated that Solar-Wind-Deep Learning systems achieved the highest average effect size of 20.653. The forecasting model achieved Root Mean Square Error (RMSE) values of 115.70–125.65 and Mean Absolute Error (MAE) values of 90.34–93.64, while training and validation losses decreased by more than 39%, indicating stable convergence. Operational analysis showed that hybrid renewable generation consistently exceeded campus charging demand, maintaining battery State of Charge (SoC) near 100% and resulting in a cumulative renewable energy generation of 43,235.30 kWh. Annual renewable energy exceeded 2,070 kWh with a maximum energy balance of 882.64 kWh. The system was economically feasible, achieving an IRR of 14.9% and positive cumulative cash flow after the seventh operational year, while reducing cumulative CO₂ emissions by more than 22,450 kg over 20 years.