Hybrid AI-based forecasting of green hydrogen and solar power using AdaBoost-BWOA
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Zhaiaibai Ma
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Ye Wang
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 R2 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 R2 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.
Acknowledgments
We would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: Authors’ contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by“ Zhaiaibai Ma and Ye Wang”. Also, the first draft of the manuscript was written by Zhaiaibai Ma. Ye Wang commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The authors do not have permissions to share data.
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