PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
ISBN 9798400700323
2 Pith papers cite this work. Polarity classification is still indexing.
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Reinforcement learning with imitation learning and reward shaping improves online workload shifting in a one-turbine one-data-center simulation but remains below an offline optimizer that sees the full day.
citing papers explorer
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A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
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Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
Reinforcement learning with imitation learning and reward shaping improves online workload shifting in a one-turbine one-data-center simulation but remains below an offline optimizer that sees the full day.