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Power-infrastructure expansion planning for training-oriented AI data centers under large-model scaling uncertainty
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DOI：10.1016/j.egyai.2026.100840.png)
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Abstract
En 中文
• A probabilistic framework links large-model scaling to training-oriented AIDC load growth. • Frontier-envelope state-space forecasting generates future parameter-scale scenarios. • CVaR-aware source–load–storage–grid planning supports power-infrastructure expansion.
Keywords:
AI data center
Training-oriented workload
Large-model parameter scale
Power-infrastructure expansion
Probabilistic forecasting
Coordinated source–load–storage–grid planning
Conditional value-at-risk
Journal
IF:
9.6
Papers:
835
Citations:
3.1K
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