Enhancing solar energy integration for hydrogen refueling stations: A novel hybrid forecasting model for accurate DNI prediction in Jiangsu Province

Abstract

Accurate prediction of direct normal irradiance (DNI) is critical for optimizing solar energy integration in hydrogen production systems. This study proposes a novel hybrid forecasting model that integrates variational mode decomposition (VMD), sample entropy (SE), biogeography-based optimization (BBO), and histogram gradient boosting regression (HGBR) to enhance the accuracy of DNI prediction. VMD is used to decompose the nonlinear solar radiation signals, while SE clusters the resulting modes based on complexity. BBO fine-tunes the hyperparameters of HGBR, which serves as the core prediction engine. Applied to a case study in Jiangsu Province, China, the model demonstrates superior forecasting performance compared to conventional models. The proposed hybrid model achieves a coefficient of determination of 0.98 and a root mean square error of 39.69 W/m². The predicted DNI values are used to optimize the design and operation of a solar-powered hydrogen refueling station (HRS), comprising the 1148-kW photovoltaic arrays, a 1000-kW proton exchange membrane, a 204-kWh battery storage system, and a 2000-kg hydrogen storage tank. These forecasts enable dynamic alignment between solar generation and hydrogen production, ensuring energy-efficient scheduling and load management. The techno-economic analysis confirms the system's feasibility, yielding a levelized cost of hydrogen of 3.20$/kg and a net present cost of 2,143,512$. The proposed hybrid model advances forecasting accuracy and can provide a scalable and cost-effective pathway for deploying sustainable hydrogen infrastructure in support of clean transportation.