DC-XPIFM: An Explainable Physics-Informed Foundation Model for AI Data-Center Power Forecasting

Rapid power fluctuations from AI workloads challenge the short-term load forecasts used to schedule reserves and coordinate grid operation. Supporting these decisions across heterogeneous AI data centers motivates the development of generalized forecasters that combine transferable temporal representations with facility-specific physical relationships. Time-series foundation models provide the former through pretraining, but do not explicitly enforce consistency between electrical and thermal dynamics. Introducing DC-XPIFM, which, to the best of the authors’ knowledge, is the first explainable physics-informed foundation model framework for short-term AI data-center power forecasting. The framework uses ordinary differential equations (ODEs) from a multi-node lumped thermal resistance-capacitance (RC) network to couple power consumption with GPU and memory temperatures, consistent with energy conservation and Newton’s law of cooling. These equations regularize fine-tuning, combining patterns learned during pretraining with electro-thermal constraints informed by local measurements. DC-XPIFM is instantiated on Chronos-2, TimesFM, and Moirai and compares each backbone with its unconstrained fine-tuned variants, alongside state-of-the-art (SOTA) forecasting baselines. PI-TimesFM achieves the lowest error among the evaluated methods at every tested look-back and prediction lengths. PI-TimesFM embeddings also significantly improve the five-class workload-probe accuracy relative to vanilla TimesFM. These results show that physics-informed forecasts more accurately capture and adhere to throttle-aware power changes, abrupt drops and recovery, and post-event stability during training, fine-tuning, and online inference, behaviors essential to responsive AI data-center power management. To the best of the authors' knowledge, this is also the first Bayesian inverse-PINN method for identifying electro-thermal RC parameters from operational AI data-center data, alongside an explainability analysis that connects forecasting gains to interpretable electro-thermal dynamics and operationally informative learned representations.