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.
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.
Research Areas
