Machine Learning Applications for Data Center Optimization
Abstract
The modern data center (DC) is a complex interaction of multiple mechanical,
electrical and controls systems. The sheer number of possible operating
configurations and nonlinear interdependencies make it difficult to understand and
optimize energy efficiency. We develop a neural network framework that learns from
actual operations data to model plant performance and predict PUE within a range of
0.004 +/0.005 (mean absolute error +/- 1 standard deviation), or 0.4% error for a
PUE of 1.1. The model has been extensively tested and validated at Google DCs. The
results demonstrate that machine learning is an effective way of leveraging
existing sensor data to model DC performance and improve energy efficiency.
