# Short-Term Load Forecasting for AI-Data Center

arXiv: https://arxiv.org/abs/2503.07756 · HTML: https://mlsanity.com/paper/2503.07756/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning](https://mlsanity.com/paper/2604.07345/) (2026)
- [A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers](https://mlsanity.com/paper/2605.04074/) (2026)
- [A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures](https://mlsanity.com/paper/2607.17391/) (2026)

## Abstract (arXiv)

Recent research shows large-scale AI-centric data centers could experience rapid fluctuations in power demand due to varying computation loads, such as sudden spikes from inference or interruption of training large language models (LLMs). As a consequence, such huge and fluctuating power demand pose significant challenges to both data center and power utility operation. Accurate short-term power forecasting allows data centers and utilities to dynamically allocate resources and power large computing clusters as required. However, due to the complex data center power usage patterns and the black-box nature of the underlying AI algorithms running in data centers, explicit modeling of AI-data center is quite challenging. Alternatively, to deal with this emerging load forecasting problem, we propose a data-driven workflow to model and predict the short-term electricity load in an AI-data center, and such workflow is compatible with learning-based algorithms such as LSTM, GRU, 1D-CNN. We validate our framework, which achieves decent accuracy on data center GPU short-term power consumption. This provides opportunity for improved power management and sustainable data center operations.