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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.03222 |
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| _version_ | 1866913298625069056 |
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| author | Carbonero, Alvaro Mao, Shaowen Mehana, Mohamed |
| author_facet | Carbonero, Alvaro Mao, Shaowen Mehana, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage Carbonero, Alvaro Mao, Shaowen Mehana, Mohamed Machine Learning Systems and Control 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. |
| title | Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2404.03222 |