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Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers

Author

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  • Rickard Brännvall

    (ICE Data Center, RISE Research Institutes of Sweden AB, 973 47 Luleå, Sweden
    EISLAB, Luleå University of Technology, 971 87 Luleå, Sweden)

  • Jonas Gustafsson

    (ICE Data Center, RISE Research Institutes of Sweden AB, 973 47 Luleå, Sweden)

  • Fredrik Sandin

    (EISLAB, Luleå University of Technology, 971 87 Luleå, Sweden)

Abstract

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs.

Suggested Citation

  • Rickard Brännvall & Jonas Gustafsson & Fredrik Sandin, 2023. "Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers," Energies, MDPI, vol. 16(5), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2255-:d:1081268
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