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Artificial Intelligence-Based Temperature Twinning and Pre-Control for Data Center Airflow Organization

Author

Listed:
  • Na Huang

    (Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
    College of Robotics, Beijing Union University, Beijing 100101, China)

  • Xiang Li

    (Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
    College of Robotics, Beijing Union University, Beijing 100101, China)

  • Quanming Xu

    (Vertiv Tech Co., Ltd., Shenzhen 518116, China)

  • Ronghao Chen

    (College of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China)

  • Huidong Chen

    (College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China)

  • Aidong Chen

    (Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
    College of Robotics, Beijing Union University, Beijing 100101, China
    Research Center for Multi-Intelligent Systems, Beijing Union University, Beijing 100101, China)

Abstract

Green and low-carbon has become the main theme of global energy development. Data centers are the core of the digital age, carrying huge arithmetic demand. Data centers must implement green low-carbon energy efficiency management to improve energy efficiency, reduce energy waste and carbon emissions, and achieve sustainable development. As a result, an intelligent management strategy for dynamic energy efficiency of data center networks with Artificial Intelligence (AI) fitting control is proposed. Firstly, a Long Short-Term Memory (LSTM) network is used for long sequence trend prediction to predict the temperature of the data center in the next sequence using the temperature of the past 15 sequences and the power consumption of the equipment as parameters. Then, based on the prediction results, the intelligent air conditioning controller based on Deep Q-Network (DQN) is designed to update the parameters by using the gradient of double-Q network and error backpropagation, and the optimal control action is selected by using the ε-greedy strategy to ensure that the prediction of the hotspot does not occur. Experiments show that the average absolute errors of temperature prediction for supply air, return air, cold aisle as well as hot aisle are 0.32 °C, 0.21 °C, 0.36 °C and 0.19 °C, respectively. The Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) decreased by an average of 2.6% and 2.5%, respectively. The method achieves the purpose of predicting future temperatures and intelligently controlling the output so that the data center can satisfy the premise of normal operation and thus achieve more efficient energy use.

Suggested Citation

  • Na Huang & Xiang Li & Quanming Xu & Ronghao Chen & Huidong Chen & Aidong Chen, 2023. "Artificial Intelligence-Based Temperature Twinning and Pre-Control for Data Center Airflow Organization," Energies, MDPI, vol. 16(16), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6063-:d:1220406
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    References listed on IDEAS

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