IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v67y2017icp625-640.html
   My bibliography  Save this article

A review of air conditioning energy performance in data centers

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S136403211630541X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2016.09.050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. 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.
    3. Manaserh, Yaman M. & Tradat, Mohammad I. & Bani-Hani, Dana & Alfallah, Aseel & Sammakia, Bahgat G. & Nemati, Kourosh & Seymour, Mark J., 2022. "Machine learning assisted development of IT equipment compact models for data centers energy planning," Applied Energy, Elsevier, vol. 305(C).
    4. 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.
    5. Aristov, Yu.I., 2021. "Adsorptive conversion of ultralow-temperature heat: Thermodynamic issues," Energy, Elsevier, vol. 236(C).
    6. 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.
    7. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    8. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    9. 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.
    10. 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).
    11. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    12. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    13. 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).
    14. Shun-Hsiung Peng & Shang-Lien Lo, 2024. "An Economic Analysis of Energy Saving and Carbon Mitigation by the Use of Phase Change Materials for Cool Energy Storage for an Air Conditioning System—A Case Study," Energies, MDPI, vol. 17(4), pages 1-17, February.
    15. 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.
    16. 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).
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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).
    23. 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.
    24. 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).
    25. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    2. 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.
    3. Di Salvo, André L.A. & Agostinho, Feni & Almeida, Cecília M.V.B. & Giannetti, Biagio F., 2017. "Can cloud computing be labeled as “green”? Insights under an environmental accounting perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 514-526.
    4. Zhang, Hainan & Shao, Shuangquan & Xu, Hongbo & Zou, Huiming & Tang, Mingsheng & Tian, Changqing, 2017. "Simulation on the performance and free cooling potential of the thermosyphon mode in an integrated system of mechanical refrigeration and thermosyphon," Applied Energy, Elsevier, vol. 185(P2), pages 1604-1612.
    5. 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).
    6. Habibi Khalaj, Ali & Abdulla, Khalid & Halgamuge, Saman K., 2018. "Towards the stand-alone operation of data centers with free cooling and optimally sized hybrid renewable power generation and energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 451-472.
    7. Matteo Manganelli & Alessandro Soldati & Luigi Martirano & Seeram Ramakrishna, 2021. "Strategies for Improving the Sustainability of Data Centers via Energy Mix, Energy Conservation, and Circular Energy," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    8. Petrović, Stefan & Colangelo, Alessandro & Balyk, Olexandr & Delmastro, Chiara & Gargiulo, Maurizio & Simonsen, Mikkel Bosack & Karlsson, Kenneth, 2020. "The role of data centres in the future Danish energy system," Energy, Elsevier, vol. 194(C).
    9. Wansheng Yang & Lin Yang & Junjie Ou & Zhongqi Lin & Xudong Zhao, 2019. "Investigation of Heat Management in High Thermal Density Communication Cabinet by a Rear Door Liquid Cooling System," Energies, MDPI, vol. 12(22), pages 1-25, November.
    10. Isazadeh, Amin & Ziviani, Davide & Claridge, David E., 2023. "Global trends, performance metrics, and energy reduction measures in datacom facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    11. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    12. Shao, Shuangquan & Liu, Haichao & Zhang, Hainan & Tian, Changqing, 2019. "Experimental investigation on a loop thermosyphon with evaporative condenser for free cooling of data centers," Energy, Elsevier, vol. 185(C), pages 829-836.
    13. Zhang, L.Y. & Liu, Y.Y. & Guo, X. & Meng, X.Z. & Jin, L.W. & Zhang, Q.L. & Hu, W.J., 2017. "Experimental investigation and economic analysis of gravity heat pipe exchanger applied in communication base station," Applied Energy, Elsevier, vol. 194(C), pages 499-507.
    14. Habibi Khalaj, Ali & Halgamuge, Saman K., 2017. "A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system," Applied Energy, Elsevier, vol. 205(C), pages 1165-1188.
    15. Shuja, Junaid & Gani, Abdullah & Shamshirband, Shahaboddin & Ahmad, Raja Wasim & Bilal, Kashif, 2016. "Sustainable Cloud Data Centers: A survey of enabling techniques and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 195-214.
    16. Uddin, Mueen & Darabidarabkhani, Yasaman & Shah, Asadullah & Memon, Jamshed, 2015. "Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1553-1563.
    17. 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.
    18. Zhen Yang & Jinhong Du & Yiting Lin & Zhen Du & Li Xia & Qianchuan Zhao & Xiaohong Guan, 2022. "Increasing the energy efficiency of a data center based on machine learning," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 323-335, February.
    19. 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.
    20. Gao, D.C. & Sun, Y.J. & Ma, Z. & Ren, H., 2021. "A review on integration and design of desiccant air-conditioning systems for overall performance improvements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:67:y:2017:i:c:p:625-640. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.