Hybrid AI-based forecasting of green hydrogen and solar power using AdaBoost-BWOA

Abstract There is a massive, accelerating restructuring of the global energy sector as it attempts to meet the pivotal need to address climate change and earn a sustainable future. Electrolysis, using renewable energy to create green hydrogen, is critical as we move to sustainability. However, reliable prediction of hydrogen production based on the solar sources remains problematic. This paper recommends a breakthrough hybrid framework based on machine learning that combines Adaptive Boosting (AdaBoost) and the Black Widow Optimization Algorithm (BWOA) for forecasting Hydrogen Production Rate (HPR) and Photovoltaic Cell Power (PPV) in the proposed solar-powered system, considering meteorological data from Abu Dhabi. The model was designed and evaluated using standardized data in an 80:20 train-test ratio as preparation and evaluation. The R 2 of the AdaBoost-BWOA model was outstanding at 0.998 for HPR and 0.998 for PPV, and RMSE value was as low as 11.282 and 2,114.667, respectively. The proposed best model (AdaBoost-BWOA) outperformed other hybrid approaches, such as MLP-BWOA, LGB-BWOA, DT-BWOA, PSO-AdaBoost, GA-AdaBoost, and DE-AdaBoost by providing significant improvements in both RMSE and R 2 values. The refined system also estimated a maximum hydrogen yield of 542.91 kg per hour in Summer. This performance validates the reliable predictive capabilities of the model that are crucial for maximizing hydrogen production in solar systems and for practical purposes in the technologies of sustainable energy.