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.

为了应对紧迫的气候变化挑战，急需从化石燃料转向可持续能源系统，其中可再生能源发挥着关键作用。然而，可再生能源的固有变动性没有有效的储能解决方案，经常导致能源供需失衡。地下氢储存（UHS）作为一种有前景的长期储存解决方案，其广泛实施受到与高保真度 UHS 模拟相关的高计算成本的阻碍。本文从数据驱动的角度介绍了 UHS，并概述了将机器学习整合到 UHS 中的路线图，从而促进 UHS 的大规模部署。