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.
Challenges with modern data centers, design considerations and recommended power system studies,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.