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

1
Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
2
Faculty of Civil and Environmental Engineering, Poznan University of Technology, 60-965 Pozanan, Poland
3
Poznan Supercomputing and Networking Center, 60-965 Poznan, Poland
*
Author to whom correspondence should be addressed.
Energies 2019, 12(5), 814; https://doi.org/10.3390/en12050814
Received: 22 January 2019 / Revised: 22 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue District Heating and Cooling Networks)
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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. View Full-Text
Keywords: data center; heat reuse; Computational Fluid Dynamics; prediction algorithm; neural networks data center; heat reuse; Computational Fluid Dynamics; prediction algorithm; neural networks
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Antal, M.; Cioara, T.; Anghel, I.; Gorzenski, R.; Januszewski, R.; Oleksiak, A.; Piatek, W.; Pop, C.; Salomie, I.; Szeliga, W. Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model. Energies 2019, 12, 814.

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