Reducing Power Consumption in Data Center by Predicting Temperature Distribution and Air Conditioner Efficiency with Machine Learning

Abstract

To reduce the power consumption in data centers, the coordinated control of the air conditioner and the servers is required. It takes tens of minutes for changes of operational parameters of air conditioners including outlet air temperature and volume to be reflected in the temperature distribution in the whole data center. So, the proactive control of the air conditioners is required according to the prediction temperature distribution corresponding to the load on the servers. In this paper, the temperature distribution and the power efficiency of air conditioner were predicted by using a machine-learning technique, and also we propose a method to follow-up proactive control of the air conditioner under the predicted optimum condition. Consequently, by the follow-up proactive control of the air conditioner and the load of servers, power consumption reduction of 30% at maximum was demonstrated.