IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2820-d554536.html
   My bibliography  Save this article

Energy Conservation Measures for a Research Data Center in an Academic Campus

Author

Listed:
  • Khaled Iyad Alsharif

    (Mechanical Engineering, Youngstown State University, Youngstown, OH 44555, USA)

  • Aspen Glaspell

    (Mechanical Engineering, Youngstown State University, Youngstown, OH 44555, USA)

  • Kyosung Choo

    (Mechanical Engineering, Youngstown State University, Youngstown, OH 44555, USA)

Abstract

Simulation and experimental studies were conducted to investigate energy consumption, develop ECMs (Energy Conservation Measures), and analyze temperature increase under a power failure scenario for a research data center at Youngstown State University. Two ECMs were developed to improve energy consumption by analyzing the thermal performance of the data center: (1) increase the return temperature in air conditioning vents; (2) provide cold aisle containment with the set point temperature increase. A transient analysis was conducted under a cooling system failure scenario to predict the temperature variation over time. The results suggest that it takes 600 s to increase the server inlet temperature by 16.1 °C for the baseline model. In addition, in the ECM #2, the maximum temperature at the server inlet did not reach 40 °C under the air conditioning system failure scenario, which is the maximum operating temperature of the ASHRAE A3 envelop.

Suggested Citation

  • Khaled Iyad Alsharif & Aspen Glaspell & Kyosung Choo, 2021. "Energy Conservation Measures for a Research Data Center in an Academic Campus," Energies, MDPI, vol. 14(10), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2820-:d:554536
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2820/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/10/2820/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghahramani, Ali & Zhang, Kenan & Dutta, Kanu & Yang, Zheng & Becerik-Gerber, Burcin, 2016. "Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings," Applied Energy, Elsevier, vol. 165(C), pages 930-942.
    2. Li, Jian & Jurasz, Jakub & Li, Hailong & Tao, Wen-Quan & Duan, Yuanyuan & Yan, Jinyue, 2020. "A new indicator for a fair comparison on the energy performance of data centers," Applied Energy, Elsevier, vol. 276(C).
    Full references (including those not matched with items on IDEAS)

    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. Ghahramani, Ali & Pantelic, Jovan & Lindberg, Casey & Mehl, Matthias & Srinivasan, Karthik & Gilligan, Brian & Arens, Edward, 2018. "Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems," Applied Energy, Elsevier, vol. 230(C), pages 42-51.
    2. Jianwu Xiong & Linlin Chen & Yin Zhang, 2023. "Building Energy Saving for Indoor Cooling and Heating: Mechanism and Comparison on Temperature Difference," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    3. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    4. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
    5. Zhang, Xiangyu & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 205(C), pages 1034-1049.
    6. 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).
    7. Romero Rodríguez, Laura & Sánchez Ramos, José & Álvarez Domínguez, Servando & Eicker, Ursula, 2018. "Contributions of heat pumps to demand response: A case study of a plus-energy dwelling," Applied Energy, Elsevier, vol. 214(C), pages 191-204.
    8. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    9. Li, Weiwei & Qian, Tong & Zhang, Yin & Shen, Yueqing & Wu, Chenghu & Tang, Wenhu, 2023. "Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty," Applied Energy, Elsevier, vol. 336(C).
    10. Zhang, Fan & de Dear, Richard & Hancock, Peter, 2019. "Effects of moderate thermal environments on cognitive performance: A multidisciplinary review," Applied Energy, Elsevier, vol. 236(C), pages 760-777.
    11. Ji, Haoran & Chen, Sirui & Yu, Hao & Li, Peng & Yan, Jinyue & Song, Jieying & Wang, Chengshan, 2022. "Robust operation for minimizing power consumption of data centers with flexible substation integration," Energy, Elsevier, vol. 248(C).
    12. Torsten Reimer & Jeonghyun Oh & Juan Pablo Loaiza-Ramírez & Hayden Barber, 2024. "Thermostat Anchors: Do Temperature Scale Characteristics Affect the Selection of Temperature Setpoints for Residential Homes?," Sustainability, MDPI, vol. 16(6), pages 1-11, March.
    13. Fan, Chengliang & Hinkelman, Kathryn & Fu, Yangyang & Zuo, Wangda & Huang, Sen & Shi, Chengnan & Mamaghani, Nasim & Faulkner, Cary & Zhou, Xiaoqing, 2021. "Open-source Modelica models for the control performance simulation of chiller plants with water-side economizer," Applied Energy, Elsevier, vol. 299(C).
    14. Guillén-Lambea, Silvia & Rodríguez-Soria, Beatriz & Marín, José M., 2017. "Comfort settings and energy demand for residential nZEB in warm climates," Applied Energy, Elsevier, vol. 202(C), pages 471-486.
    15. Katharina Boudier & Sabine Hoffmann, 2022. "Analysis of the Potential of Decentralized Heating and Cooling Systems to Improve Thermal Comfort and Reduce Energy Consumption through an Adaptive Building Controller," Energies, MDPI, vol. 15(3), pages 1-28, February.
    16. Nick Van Loy & Griet Verbeeck & Elke Knapen, 2021. "Personal Heating in Dwellings as an Innovative, Energy-Sufficient Heating Practice: A Case Study Research," Sustainability, MDPI, vol. 13(13), pages 1-27, June.
    17. Anatolijs Borodinecs & Arturs Palcikovskis & Andris Krumins & Deniss Zajecs & Kristina Lebedeva, 2024. "Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method," Clean Technol., MDPI, vol. 6(2), pages 1-12, June.
    18. Zhao, Dongliang & Lu, Xing & Fan, Tianzhu & Wu, Yuen Shing & Lou, Lun & Wang, Qiuwang & Fan, Jintu & Yang, Ronggui, 2018. "Personal thermal management using portable thermoelectrics for potential building energy saving," Applied Energy, Elsevier, vol. 218(C), pages 282-291.
    19. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    20. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.

    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:gam:jeners:v:14:y:2021:i:10:p:2820-:d:554536. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.