How thermal simulation helps optimize Meta’s data centers

This post introduces a physics-based thermal simulator, combined with data science, for optimizing Meta's data center cooling and thermal behavior. It details the methodology of building the simulator using Modelica and control Description Language, and demonstrates its predictive accuracy through a case study of a severe winter storm in Texas, achieving a Mean Absolute Error (MAE) of 0.5°F for supply air temperature. The simulator can be used to test control policies with AI or model predictive control, and future research aims to couple it with machine learning methods for real-time optimization and testing new facility designs.