How thermal simulation helps optimize Meta’s data centers

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This post details the development and application of a dynamic thermal simulator for Meta's data centers. The simulator combines physics-based models (using equations for thermal processes and building-modeling languages like Modelica) with statistical data science to replicate cooling and thermal behavior. It enables testing of control policies (AI and model predictive control) and accurately predicts performance in extreme scenarios, including for unbuilt facilities. The simulator was validated against real-world data, including a severe winter storm in Texas, showing a mean absolute error of 0.5°F for supply air temperature over a 15-day period and 1.3°F across 24 random dates. Future research aims to couple these models with machine learning methods like reinforcement learning for real-time optimization of energy and water consumption, and to test new facility and equipment designs.