Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage
<|reference_start|>Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage: To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role. However, the inherent variability of renewable energy, without effective storage solutions, often leads to imbalances between energy supply and demand. Underground Hydrogen Storage (UHS) emerges as a promising long-term storage solution to bridge this gap, yet its widespread implementation is impeded by the high computational costs associated with high fidelity UHS simulations. This paper introduces UHS from a data-driven perspective and outlines a roadmap for integrating machine learning into UHS, thereby facilitating the large-scale deployment of UHS.<|reference_end|>
@article{carbonero2024enabling,
    title={Enabling Clean Energy Resilience with Machine Learning-Empowered
  Underground Hydrogen Storage},
    author={Alvaro Carbonero, Shaowen Mao, Mohamed Mehana},
    journal={arXiv preprint arXiv:2404.03222},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03222},
    primaryClass={cs.LG cs.SY eess.SY}
}