Grid-Oriented Probabilistic Load Forecasting for AI Data Centers
How to prepare an accurate paper press kit? Illustrated guide
Qingyi Yao, Ding Lin, Tianqiao Zhao, Meng Yue, Jianhui Wang · arXiv: 2611.08929v1 · Summary page
Why cover this story this week?
For the grid, uncertainty needs a timeline
Power forecasts for computing facilities must describe not just how uncertain demand is, but how it changes from moment to moment. A benchmark built from measured power at a 448-GPU cluster follows forecasts through uncertainty calibration, generation of possible demand trajectories, and reserve procurement. After utilization changed, a nominal 90% prediction interval covered only 63.4% of observations; online calibration raised coverage to 87.3%.
Yet better coverage was not enough. Sampling each future demand independently generated unrealistic power ramps and encouraged excessive reserve purchases. Reconstructing temporal relationships from historical forecast errors lowered realized reserve cost by 7.4-10.6%. The broader lesson extends beyond data centers: a forecast can describe individual outcomes reasonably well while still misrepresenting the sequences that infrastructure operators must survive.
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