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Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers
RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.ORCID iD: 0000-0003-4293-6408
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-9759-5594
Luleå University of Technology, Sweden.
2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 5, article id 2255Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2023. Vol. 16, no 5, article id 2255
Keywords [en]
edge data center, heat management, heat reuse, modular machine learning, transferable machine learning, recurrent neural network, transfer learning, meta-learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-64222DOI: 10.3390/en16052255OAI: oai:DiVA.org:ri-64222DiVA, id: diva2:1742680
Note

Funding: Vinnova through the Celtic Next project AI-NET Aniara with project-ID C2019/3-2

Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-08-28Bibliographically approved

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Brännvall, RickardGustafsson, Jonas

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