Data Center Cooling System Energy Optimization

Our client is KocSistem (part of Koc Group, a Fortune Global 500 company)
  • Data centers generate significant amount of heat that must be removed to keep the servers running. Cooling is a high energy consuming operation
  • Suboptimal temperature setpoints (overcooling and undercooling) creates inefficiencies
  • Data centers have complex, often non-linear thermal dynamics which complicates the cooling process
  • The approach combined prediction and optimization models
  • Neural network models were implemented to predict the aftermath of temperature set point changes. Optimization models were used to determine  the optimal set point
  • This dynamic decision making allowed the environment to adapt changes in the computational load and external factors (i.e. weather conditions)
Tool Stack
  • Kafka, Python, Grafana



Saving in Energy Consumption



Increased Equipment Lifetime



Increased Server Availability