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Statistical analysis for predicting location-specific data center PUE and its improvement potential

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  • Lei, Nuoa
  • Masanet, Eric

Abstract

This paper presents a statistical framework for predictive analysis of data center power usage effectiveness (PUE), with a focus on hyperscale data centers (HDCs). Thermodynamics-based PUE models considering representative economizer choices are proposed, taking climate variables and energy system parameters as inputs for robust PUE predictions. Sobol’s method is used to assess total order sensitivity indices of key modeling parameters, suggesting that climate variables and uninterruptible power supply (UPS) efficiencies are the most important parameters. The PUE values of 17 HDCs operated by Google and Facebook were predicted, considering location-specific weather conditions, and uncertainties in energy system parameters and economizer choices. Results were verified using reported PUE values, indicating the model’s effectiveness in capturing regional and seasonal PUE variations, and in generating point-estimations for macro-level data center (DC) energy models. Finally, achievable PUE values were computed through differential evolution, identifying minimum practical PUE values that could be obtained with state-of-the-art technologies. The framework can be applied in predictions of location-specific PUE values, PUE improvement analysis, and PUE target-setting by policy makers.

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  • Lei, Nuoa & Masanet, Eric, 2020. "Statistical analysis for predicting location-specific data center PUE and its improvement potential," Energy, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:energy:v:201:y:2020:i:c:s0360544220306630
    DOI: 10.1016/j.energy.2020.117556
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    1. Xiaofei Huang & Junwei Yan & Xuan Zhou & Yixin Wu & Shichen Hu, 2023. "Cooling Technologies for Internet Data Center in China: Principle, Energy Efficiency, and Applications," Energies, MDPI, vol. 16(20), pages 1-31, October.
    2. 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.
    3. Zhao, Wenxuan & Li, Hangxin & Wang, Shengwei, 2022. "A comparative analysis on alternative air-conditioning systems for high-tech cleanrooms and their performance in different climate zones," Energy, Elsevier, vol. 261(PA).
    4. Alexandre F. Santos & Pedro D. Gaspar & Heraldo J. L. Souza, 2021. "Measuring the Energy Efficiency of Evaporative Systems through a New Index—EvaCOP," Energies, MDPI, vol. 14(9), pages 1-12, May.
    5. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu & Shen, Boyang, 2022. "A 10 MW class data center with ultra-dense high-efficiency energy distribution: Design and economic evaluation of superconducting DC busbar networks," Energy, Elsevier, vol. 250(C).
    6. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Zou, Zhice & Shen, Boyang & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu, 2022. "Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center," Applied Energy, Elsevier, vol. 310(C).
    7. Lei, Nuoa & Masanet, Eric & Koomey, Jonathan, 2021. "Best practices for analyzing the direct energy use of blockchain technology systems: Review and policy recommendations," Energy Policy, Elsevier, vol. 156(C).
    8. 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).

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