Toward Physics-Informed Machine Learning for Data Center Operations: A Tropical Case Study
This work addresses cooling cost and reliability issues for data center operators in tropical climates, representing an incremental improvement by combining existing physics with machine learning.
The paper tackles the challenge of operating data centers in tropical regions, where high temperatures and humidity increase cooling costs, by proposing a physics-informed machine learning system that integrates physical characteristics into data-driven models, and demonstrates its effectiveness in a case study on an industry-grade tropical data center.
Data centers are the backbone of computing capacity. Operating data centers in the tropical regions faces unique challenges due to consistently high ambient temperature and elevated relative humidity throughout the year. These conditions result in increased cooling costs to maintain the reliability of the computing systems. While existing machine learning-based approaches have demonstrated potential to elevate operations to a more proactive and intelligent level, their deployment remains dubious due to concerns about model extrapolation capabilities and associated system safety issues. To address these concerns, this article proposes incorporating the physical characteristics of data centers into traditional data-driven machine learning solutions. We begin by introducing the data center system, including the relevant multiphysics processes and the data-physics availability. Next, we outline the associated modeling and optimization problems and propose an integrated, physics-informed machine learning system to address them. Using the proposed system, we present relevant applications across varying levels of operational intelligence. A case study on an industry-grade tropical data center is provided to demonstrate the effectiveness of our approach. Finally, we discuss key challenges and highlight potential future directions.