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Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center

Author

Listed:
  • Leehter Yao

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Jin-Hao Huang

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1474-:d:224071
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    References listed on IDEAS

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    1. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    2. Durand-Estebe, Baptiste & Le Bot, Cédric & Mancos, Jean Nicolas & Arquis, Eric, 2014. "Simulation of a temperature adaptive control strategy for an IWSE economizer in a data center," Applied Energy, Elsevier, vol. 134(C), pages 45-56.
    3. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    4. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Adaptive full-range decoupled ventilation strategy and air-conditioning systems for cleanrooms and buildings requiring strict humidity control and their performance evaluation," Energy, Elsevier, vol. 168(C), pages 883-896.
    5. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    6. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    7. Habibi Khalaj, Ali & Scherer, Thomas & K. Halgamuge, Saman, 2016. "Energy, environmental and economical saving potential of data centers with various economizers across Australia," Applied Energy, Elsevier, vol. 183(C), pages 1528-1549.
    8. 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.
    9. Khan, Muhammad Waqas & Choudhry, Mohammad Ahmad & Zeeshan, Muhammad & Ali, Ahsan, 2015. "Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit," Energy, Elsevier, vol. 81(C), pages 477-488.
    10. Liu, Yuting & Yang, Xu & Li, Junming & Zhao, Xudong, 2018. "Energy savings of hybrid dew-point evaporative cooler and micro-channel separated heat pipe cooling systems for computer data centers," Energy, Elsevier, vol. 163(C), pages 629-640.
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    Cited by:

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    3. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.

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