Physical and Data-Driven Models for Edge Data Center Cooling System

Mikko Siltala, Rickard Brannvall*, Jonas Gustafsson, Quan Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Edge data centers are expected to become prevalent providing low latency computing power for 5G mobile and IoT applications. This article develops two models for the complete cooling system of an edge data center: One model based on the laws of thermodynamics and one data-driven model based on LSTM neural networks. The models are validated against an actual edge data center experimental set-up showing root mean squared errors (RMSE) for most individual components below 1 °C over a simulation period of approximately 10 hours; which compares favourably to state-of-the-art models.

Original languageEnglish
Title of host publication2020 Swedish Workshop on Data Science, SweDS 2020
PublisherIEEE
Number of pages7
ISBN (Electronic)9781728192048
DOIs
Publication statusPublished - 29 Oct 2020
MoE publication typeA4 Article in a conference publication
EventSwedish Workshop on Data Science - Lulea, Sweden
Duration: 29 Oct 202030 Oct 2020

Workshop

WorkshopSwedish Workshop on Data Science
Abbreviated titleSweDS
CountrySweden
CityLulea
Period29/10/202030/10/2020

Keywords

  • Cooling System
  • Data center
  • Edge
  • LSTM
  • Thermal Energy Storage

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