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.
Short-term load forecasting for ai-data center
3 Pith papers cite this work. Polarity classification is still indexing.
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A regime-adaptive ensemble with weight-learned neural network and incremental feature engineering reduces minute-scale AI data center load forecasting errors to below 1% on the MIT Supercloud dataset.
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.
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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Regime-Adaptive Weighted Ensemble Learning for Computing-Driven Dynamic Load Forecasting in AI Data Centers
A regime-adaptive ensemble with weight-learned neural network and incremental feature engineering reduces minute-scale AI data center load forecasting errors to below 1% on the MIT Supercloud dataset.
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Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.