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.

翻译：为应对气候变化的紧迫挑战，亟需从化石燃料向可持续能源体系转型，其中可再生能源发挥着关键作用。然而，若缺乏有效的储能方案，可再生能源固有的波动性常导致能源供需失衡。地下氢能储存作为极具前景的长期储能方案，有望弥合这一缺口，但其大规模应用受限于高保真地下氢能储存模拟带来的高昂计算成本。本文从数据驱动视角引入地下氢能储存概念，并勾勒出将机器学习融入地下氢能储存的路线图，从而推动其大规模部署。