Artificial intelligence-driven short-term energy forecasting of an off-grid solar PV/hydrogen fuel cell-powered AI-data center: AI-energy Nexus
Chaouki GHENAI
Abstract. Powering AI data centers with renewable hydrogen is a promising energy option. This strategy has the potential to satisfy the high energy requirements of AI-data centers while simultaneously reducing the amount of carbon emissions produced. In order to keep up with the demand for green hydrogen, well-planned infrastructure, investments in renewable energy, electrolyzes, fuel cells, and hydrogen storage are essential. In order to maximize green hydrogen production, decrease operational costs, increase energy efficiency, precise solar power forecasting and green hydrogen production are crucial. The main objective of this study is to assess the performance of green hydrogen power systems to meet the load of AI-data centers and to develop AI-based short term energy forecasting models. This study models and simulates the proposed clean power system to evaluate the performance of AI-data centers. This study will examine the technological, financial, and environmental elements of the proposed green hydrogen-based power system for the AI-data center’s massive power requirement and the proposed energy forecasting models. The challenges associated with power and heat management of AI data center and emerging and sustainable solutions for incorporating clean energy technologies and energy efficiency will be discussed in more detail.
Keywords
AI Data Center, Power Management, Green Hydrogen, Solar PV, Fuel Cell, AI-Based Forecasting, Carbon Emissions, Sustainability
Published online 6/20/2026, 5 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Chaouki GHENAI, Artificial intelligence-driven short-term energy forecasting of an off-grid solar PV/hydrogen fuel cell-powered AI-data center: AI-energy Nexus, Materials Research Proceedings, Vol. 67, pp 55-59, 2026
DOI: https://doi.org/10.21741/9781644904176-8
The article was published as article 8 of the book Climate Action and Sustainability
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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