Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations
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AI's energy footprint is directly linked to HPC overhead and model complexity and is influenced by data center energy mix and water usage.
“This is directly linked to HPC overhead and model complexity (Sections 3.1.2and 3.1.5)and is influenced by data center energy mix and water usage [56,57].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [56]
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Flagged first, then by citations
- Authors
- Kwon, S.
- Affiliations
- Binghamton University (US)
- Corresponding author
- Kwon, Soongeol
- ORCID
- Kwon, Soongeol
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 276
- Pages
- 115424
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3040891731
- Cited by
- 103
- References
- 39
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 28 · 2023: 10 · 2022: 20 · 2021: 9
- FWCI
- 9.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- data center energy efficiency, renewable energy utilization, solar power generation, battery energy storage, quality of service guarantees, two-stage stochastic programming, chance constraints, time-varying workloads, energy cost reduction
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