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

Rickard Brännvall (), Jonas Gustafsson and Fredrik Sandin
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Rickard Brännvall: ICE Data Center, RISE Research Institutes of Sweden AB, 973 47 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

Energies, 2023, vol. 16, issue 5, 1-24

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.

Keywords: edge data center; heat management; heat reuse; modular machine learning; transferable machine learning; recurrent neural network; transfer learning; meta-learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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