Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model

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ID: 113181
2019
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Abstract
This paper addresses the problem of data centers’ cost efficiency considering the potential of reusing the generated heat in district heating networks. We started by analyzing the requirements and heat reuse potential of a high performance computing data center and then we had defined a heat reuse model which simulates the thermodynamic processes from the server room. This allows estimating by means of Computational Fluid Dynamics simulations the temperature of the hot air recovered by the heat pumps from the server room allowing them to operate more efficiently. To address the time and space complexity at run-time we have defined a Multi-Layer Perceptron neural network infrastructure to predict the hot air temperature distribution in the server room from the training data generated by means of simulations. For testing purposes, we have modeled a virtual server room having a volume of 48 m3 and two typical 42U racks. The results show that using our model the heat distribution in the server room can be predicted with an error less than 1 °C allowing data centers to accurately estimate in advance the amount of waste heat to be reused and the efficiency of heat pump operation.
Reference Key
antal2019energiesreuse Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marcel Antal;Tudor Cioara;Ionut Anghel;Radoslaw Gorzenski;Radoslaw Januszewski;Ariel Oleksiak;Wojciech Piatek;Claudia Pop;Ioan Salomie;Wojciech Szeliga;Antal, Marcel;Cioara, Tudor;Anghel, Ionut;Gorzenski, Radoslaw;Januszewski, Radoslaw;Oleksiak, Ariel;Piatek, Wojciech;Pop, Claudia;Salomie, Ioan;Szeliga, Wojciech;
Journal energies
Year 2019
DOI
10.3390/en12050814
URL
https://www.mdpi.com/1996-1073/12/5/814#citedby
Keywords
computational fluid dynamics neural networks data center heat reuse prediction algorithm

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