An Intelligent Thermal Management Strategy for a Data Center Prototype Based on Digital Twin Technology
Data centers contribute to roughly 1% of global energy consumption and 0.3% of worldwide carbon dioxide emissions. The cooling system alone constitutes a substantial 50% of total energy consumption for data centers. Lowering Power Usage Effectiveness (PUE) of data center cooling systems from 2.2 to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7675 |
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| Summary: | Data centers contribute to roughly 1% of global energy consumption and 0.3% of worldwide carbon dioxide emissions. The cooling system alone constitutes a substantial 50% of total energy consumption for data centers. Lowering Power Usage Effectiveness (PUE) of data center cooling systems from 2.2 to 1.4, or even below, is one of the critical issues in this thermal management area. In this work, a digital twin system of an Intelligent Data Center (IDC) prototype is designed to be capable of real-time monitoring the temperature distribution. Moreover, aiming to lower PUE, Deep Q-Learning Network (DQN) is further established to make optimization decisions of thermal management during cooling down of the local hotspot. The entire process of thermal management for IDC can be real-time visualized in Unity, forming the virtual entity of data center prototype, which provides an intelligent solution for sustainable data center operation. |
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| ISSN: | 2076-3417 |