Data Center Air Handling Unit Fan Speed Optimization Using Machine Learning Techniques

Abstract

An Increase in Data Center power requirements has placed significant pressure on traditional Data Center cooling management Systems. The temperature in the Data Center is controlled using Air handling units (AHUs) and plays a critical role in a Data Center to maintain the required temperature to ensure the best possible performance. As the targeted Data Center is quite Old and using backdated technologies and does not have sensor-based technologies implemented. One of the issues faced by the target Data Center was that AHU fan speed was set to the static setting which impacts the Supplied Temperature in Data Center and results in excessive hot & cold temperature inside a Data Center. The proposed model resolves the problem faced by the targeted Data Center to operate the AHU fan speed to maintain the required DC Temperature on the predicted range by using machine learning techniques. This model not only solves the problem of maintaining the necessary temperature in the Data Center, but it can also regulate the fan speed within the expected range, contributing to the Data Center's energy efficiency. Supervised machine learning with linear regression and logistical regression approaches are utilized to investigate which methodology produces the best prediction results for adjusting the AHU unit fan speed for better control of the supplied Data Center temperature. In the targeted Data Center, it has no scope to expand more rack space or host IT load. It is desired that the predicted or recommended range for controlling AHU fan speed be determined so that the needed temperature can be sustained with the suggested setting without requiring extensive manual task. Henceforth as the data generated by the Data Center is historical, supervised regression machine learning models using Linear and Logistic Regression techniques are used. Both regression models are compared to see which regression methodology predicts the best variable fan speed range for maintaining the data center's required temperature.