# Dynamic Load Model for Data Centers with Pattern-Consistent Calibration

arXiv: https://arxiv.org/abs/2602.07859 · HTML: https://mlsanity.com/paper/2602.07859/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://mlsanity.com/paper/2312.15709/) (2023)
- [Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects](https://mlsanity.com/paper/2509.07218/) (2025)
- [The MIT Supercloud Dataset](https://mlsanity.com/paper/2108.02037/) (2021)
- [Modeling and Analysis of Utilizing Cryptocurrency Mining for Demand Flexibility in Electric Energy Systems: A Synthetic Texas Grid Case Study](https://mlsanity.com/paper/2207.02428/) (2022)
- [Enhancing Data Center Low-Voltage Ride-Through](https://mlsanity.com/paper/2510.03867/) (2025)
- [Data Center Model for Transient Stability Analysis of Power Systems](https://mlsanity.com/paper/2505.16575/) (2025)
- [A Theoretical Framework for Virtual Power Plant Integration with Gigawatt-Scale AI Data Centers: Multi-Timescale Control and Stability Analysis](https://mlsanity.com/paper/2506.17284/) (2025)
- [Wide-Area Power System Oscillations from Large-Scale AI Workloads](https://mlsanity.com/paper/2508.16457/) (2025)
- [Operational Risks in Grid Integration of Large Data Center Loads: Characteristics, Stability Assessments, and Sensitivity Studies](https://mlsanity.com/paper/2510.05437/) (2025)
- [Grid Operational Benefit Analysis of Data Center Spatial Flexibility: Congestion Relief, Renewable Energy Curtailment Reduction, and Cost Saving](https://mlsanity.com/paper/2511.08759/) (2025)
- [From Liability to Asset: A Three-Mode Grid-Forming Control Framework for Centralized Data Center UPS Systems](https://mlsanity.com/paper/2512.16497/) (2025)
- [Enhancing Grid Resilience for Giga-Watt Scale Data Centers Using High Voltage Circuit Breaker Operated Braking Resistors](https://mlsanity.com/paper/2512.21295/) (2025)

## Abstract (arXiv)

The rapid growth of data centers has made large electronic load (LEL) modeling increasingly important for power system analysis. Such loads are characterized by fast workload-driven variability and protection-driven disconnection and reconnection behavior that are not captured by conventional load models. Existing data center load modeling includes physics-based approaches, which provide interpretable structure for grid simulation, and data-driven approaches, which capture empirical workload variability from data. However, physics-based models are typically uncalibrated to facility-level operation, while trajectory alignment in data-driven methods often leads to overfitting and unrealistic dynamic behavior. To resolve these limitations, we design the framework to leverage both physics-based structure and data-driven adaptability. The physics-based structure is parameterized to enable data-driven pattern-consistent calibration from real operational data, supporting facility-level grid planning. We further show that trajectory-level alignment is limited for inherently stochastic data center loads. Therefore, we design the calibration to align temporal and statistical patterns using temporal contrastive learning (TCL). This calibration is performed locally at the facility, and only calibrated parameters are shared with utilities, preserving data privacy. The proposed load model is calibrated by real-world operational load data from the MIT Supercloud, ASU Sol, Blue Waters, and ASHRAE datasets. Then it is integrated into the ANDES platform and evaluated on the IEEE 39-bus, NPCC 140-bus, and WECC 179-bus systems. We find that interactions among LELs can fundamentally alter post-disturbance recovery behavior, producing compound disconnection-reconnection dynamics and delayed stabilization that are not captured by uncalibrated load models.