Dynamic thermal environment management technologies for data center: A review

The energy demand of the data center (DC) industry has accounted for 2% of the total global energy consumption, and its operating power consumption has reached 50 times that of the commercial buildings. Therefore, improving the thermal environment performance to reduce the energy cost is major trend to realize the energy savings of DC. It has become a positive measure to integrate smart technologies to improve the operating efficiency of HVAC systems, especially due to the development of informatization and intelligence. This study provides detailed overview of the dynamic thermal environment management technologies of the DCs. The requirements of thermal environment in DCs were reviewed by different levels, which are the basis of the design and operation. Accordingly, various dynamic prediction technologies were presented concerning the theoretical mechanics, the computation efficiency and the applicable circumstances. The performance of the PID, the model predictive control and the reinforcement learning in the dynamic thermal environment control of DC were analyzed. Moreover, the evaluation systems based on cooling capacity, exergy analysis and entransy analysis were introduced respectively to measure the performance of the thermal environment. By investigating the knowledge related to the “requirement, prediction, control, and evaluation” of the DC dynamic thermal environment, this study aims to provide reference and guidance for energy conservation, airflow organization and smart operation of DCs.