A sequence-to-point knowledge distillation approach trains compact student models to match the short-term forecasting performance of larger teacher models while cutting model size and memory by over 10x on AI data center load data.
Ai, data centers, and the U.S. electric grid: A watershed moment,
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Deployment-Efficient Short-Term Load Forecasting in AI Data Centers via Sequence-to-Point Knowledge Distillation
A sequence-to-point knowledge distillation approach trains compact student models to match the short-term forecasting performance of larger teacher models while cutting model size and memory by over 10x on AI data center load data.