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Review Papers | Geotechnical Engineering | United States of America | Volume 14 Issue 3, March 2025 | Popularity: 7 / 10
Geothermal Energy Integration in Data Centers: A Pathway to Carbon-Neutral and AI-Optimized Cooling Systems
Mamta Chauhan
Abstract: The exponential rise of artificial intelligence (AI), cloud computing, and big data is driving unprecedented energy demand in data centers. By 2030, data centers are projected to consume 10% of the world?s electricity, with cooling systems alone accounting for nearly 40% of total energy consumption (International Energy Agency [IEA], 2024). Conventional cooling solutions, such as mechanical and evaporative cooling, contribute significantly to carbon emissions, high operational costs, and water resource depletion (Ghamkhari & Mohsenian-Rad, 2012). Geothermal energy, specifically ground-source heat exchangers (GSHEs) and enhanced geothermal systems (EGS), presents a low-carbon, cost-effective alternative for data center cooling. This study explores AI-driven geothermal reservoir optimization, Computational Fluid Dynamics (CFD) modeling, and lifecycle cost analysis to assess the feasibility of geothermal-powered cooling in data centers (National Renewable Energy Laboratory [NREL], 2024). Findings indicate that geothermal cooling can reduce energy consumption by 40?50%, while AI-powered reservoir management can enhance efficiency by 25%, significantly reducing operational costs (Google LLC, 2023). Additionally, this paper evaluates case studies from Google, Microsoft, Fervo Energy, and Amazon, assessing policy frameworks, financial models, and regulatory incentives for geothermal adoption. Future research must focus on hybrid geothermal-solar-wind integration, AI-automated geothermal drilling, and real-time energy optimization for next-generation carbon-neutral data centers.
Keywords: Geothermal energy; data centers; enhanced geothermal systems (EGS); AI-driven optimization; computational fluid dynamics (CFD); hybrid renewable integration; sustainable cooling; lifecycle cost analysis; policy incentives
Edition: Volume 14 Issue 3, March 2025
Pages: 1701 - 1704
DOI: https://www.doi.org/10.21275/SR25328043456
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