Optimization Control Strategies and Evaluation Metrics of Cooling Systems in Data Centers: A Review

Qiankun Chang, Yuanfeng Huang, Kaiyan Liu (), Xin Xu, Yaohua Zhao and Song Pan
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Qiankun Chang: Dawning Information Industry Co., Ltd., Beijing 100193, China
Yuanfeng Huang: Sugon DataEnergy (Beijing) Co., Ltd., Beijing 100193, China
Kaiyan Liu: The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
Xin Xu: Dawning Information Industry Co., Ltd., Beijing 100193, China
Yaohua Zhao: The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
Song Pan: The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China

Sustainability, 2024, vol. 16, issue 16, 1-41

Abstract: In the age of digitalization and big data, cooling systems in data centers are vital for maintaining equipment efficiency and environmental sustainability. Although many studies have focused on the classification and optimization of data center cooling systems, systematic reviews using bibliometric methods are relatively scarce. This review uses bibliometric analysis to explore the classifications, control optimizations, and energy metrics of data center cooling systems, aiming to address research gaps. Using CiteSpace and databases like Scopus, Web of Science, and IEEE, this study maps the field’s historical development and current trends. The findings indicate that, firstly, the classification of cooling systems, optimization strategies, and energy efficiency metrics are the current focal points. Secondly, this review assesses the applicability of air-cooled and liquid-cooled systems in different operational environments, providing practical guidance for selection. Then, for air cooling systems, the review demonstrates that optimizing the design of static pressure chamber baffles has significantly improved airflow uniformity. Finally, the article advocates for expanding the use of artificial intelligence and machine learning to automate data collection and energy efficiency analysis, it also calls for the global standardization of energy efficiency metrics. This study offers new perspectives on the design, operational optimization, and performance evaluation of data center cooling systems.

Keywords: data center; cooling system; energy efficiency assessment; control strategy optimization; bibliometrics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:16:p:7222-:d:1461735

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