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A review of air conditioning energy performance in data centers

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  • Ni, Jiacheng
  • Bai, Xuelian

Abstract

During the last years, many countries are experiencing rapid expansions in the number and size of data centers to keep pace with their internet and cloud computing needs. High energy consumption of the data center has gradually attracted public attention. However, there are no common efficiency standards governing the design or operation of data centers and the associated air conditioning systems. And the statistical research on air conditioning energy performance is still sorely lacking. This paper presents a summary of 100 data centers air conditioning energy performance. Energy efficiency metrics and benchmarks are also provided so that operators can use these information to track the performance of and identify opportunities to reduce energy use of air conditioning systems in their data centers. The collected data from articles and reports show that the average of HVAC system effectiveness index is 1.44. More than half of the data centers’ air conditioning systems are inefficient. In total, HVAC systems account for about 38% of facility energy consumption. The range for this usage was 21% for the most efficient system and 61% for the least efficient system. Moreover it would be necessary to review some currently available energy efficiency strategies such as economizer cycles, airflow optimization, energy management, and simulations tools.

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  • Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
    • Handle: RePEc:eee:rensus:v:67:y:2017:i:c:p:625-640
    DOI: 10.1016/j.rser.2016.09.050
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    References listed on IDEAS

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    1. Zhang, Hainan & Shao, Shuangquan & Xu, Hongbo & Zou, Huiming & Tian, Changqing, 2014. "Free cooling of data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 171-182.
    2. Kim, Min-Hwi & Ham, Sang-Woo & Park, Jun-Seok & Jeong, Jae-Weon, 2014. "Impact of integrated hot water cooling and desiccant-assisted evaporative cooling systems on energy savings in a data center," Energy, Elsevier, vol. 78(C), pages 384-396.
    3. Oró, Eduard & Depoorter, Victor & Garcia, Albert & Salom, Jaume, 2015. "Energy efficiency and renewable energy integration in data centres. Strategies and modelling review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 429-445.
    4. Ham, Sang-Woo & Kim, Min-Hwi & Choi, Byung-Nam & Jeong, Jae-Weon, 2015. "Energy saving potential of various air-side economizers in a modular data center," Applied Energy, Elsevier, vol. 138(C), pages 258-275.
    5. Rong, Huigui & Zhang, Haomin & Xiao, Sheng & Li, Canbing & Hu, Chunhua, 2016. "Optimizing energy consumption for data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 674-691.
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    3. Cheung, Howard & Wang, Shengwei & Zhuang, Chaoqun & Gu, Jiefan, 2018. "A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation," Applied Energy, Elsevier, vol. 222(C), pages 329-342.
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    5. Jing Ni & Bowen Jin & Bo Zhang & Xiaowei Wang, 2017. "Simulation of Thermal Distribution and Airflow for Efficient Energy Consumption in a Small Data Centers," Sustainability, MDPI, vol. 9(4), pages 1-16, April.
    6. Zhiyuan Liu & Hang Yu & Rui Liu & Meng Wang & Chaoen Li, 2020. "Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations," Energies, MDPI, vol. 13(2), pages 1-22, January.
    7. Jerez Monsalves, Juan & Bergaentzlé, Claire & Keles, Dogan, 2023. "Impacts of flexible-cooling and waste-heat recovery from data centres on energy systems: A Danish case study," Energy, Elsevier, vol. 281(C).
    8. Aristov, Yu.I., 2021. "Adsorptive conversion of ultralow-temperature heat: Thermodynamic issues," Energy, Elsevier, vol. 236(C).
    9. Zhang, Hainan & Shao, Shuangquan & Tian, Changqing & Zhang, Kunzhu, 2018. "A review on thermosyphon and its integrated system with vapor compression for free cooling of data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 789-798.
    10. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    11. Atta ul Mannan Hashmi & Arshan Ahmed & Fahad Rafi Butt & Shahbaz Ghani & Imran Akhtar, 2022. "Flow Analysis of Various Inlet Velocity Profiles on Indoor Temperature for Energy Conservation of HVAC System Using CFD," International Journal of Innovations in Science & Technology, 50sea, vol. 3(Special I), pages 187-196, february.
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    13. Jing Ni & Bowen Jin & Shanglei Ning & Xiaowei Wang, 2019. "The Numerical Simulation of the Airflow Distribution and Energy Efficiency in Data Centers with Three Types of Aisle Layout," Sustainability, MDPI, vol. 11(18), pages 1-13, September.
    14. Hernández-Romero, Ilse María & Fuentes-Cortés, Luis Fabián & Nápoles-Rivera, Fabricio, 2019. "Conditions accommodating a dominant stakeholder in the design of renewable air conditioning systems for tourism complexes," Energy, Elsevier, vol. 172(C), pages 808-822.
    15. Alberto Cocaña-Fernández & Emilio San José Guiote & Luciano Sánchez & José Ranilla, 2019. "Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques," Energies, MDPI, vol. 12(11), pages 1-21, June.
    16. M. Hasan Jamal & M. Tayyab Chaudhry & Usama Tahir & Furqan Rustam & Soojung Hur & Imran Ashraf, 2022. "Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers," Energies, MDPI, vol. 15(7), pages 1-20, March.
    17. Shunling Ruan & Haiyan Xie & Song Jiang, 2017. "Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    18. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    19. Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2019. "Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD," Energies, MDPI, vol. 12(8), pages 1-17, April.
    20. Tong, Zhen & Wang, Wencheng & Fang, Chunxue, 2023. "Energy-saving potential analysis of a CO2 two-phase thermosyphon loop system used in data centers," Energy, Elsevier, vol. 275(C).
    21. Mahbod, Muhammad Haiqal Bin & Chng, Chin Boon & Lee, Poh Seng & Chui, Chee Kong, 2022. "Energy saving evaluation of an energy efficient data center using a model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 322(C).
    22. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    23. Ye, Guisen & Gao, Feng & Fang, Jingyang, 2022. "A mission-driven two-step virtual machine commitment for energy saving of modern data centers through UPS and server coordinated optimizations," Applied Energy, Elsevier, vol. 322(C).
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    25. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).

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