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

Marcel Antal, Tudor Cioara, Ionut Anghel, Radoslaw Gorzenski, Radoslaw Januszewski, Ariel Oleksiak, Wojciech Piatek, Claudia Pop, Ioan Salomie and Wojciech Szeliga
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Marcel Antal: Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
Tudor Cioara: Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
Ionut Anghel: Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
Radoslaw Gorzenski: Faculty of Civil and Environmental Engineering, Poznan University of Technology, 60-965 Pozanan, Poland
Radoslaw Januszewski: Poznan Supercomputing and Networking Center, 60-965 Poznan, Poland
Ariel Oleksiak: Poznan Supercomputing and Networking Center, 60-965 Poznan, Poland
Wojciech Piatek: Poznan Supercomputing and Networking Center, 60-965 Poznan, Poland
Claudia Pop: Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
Ioan Salomie: Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
Wojciech Szeliga: Poznan Supercomputing and Networking Center, 60-965 Poznan, Poland

Energies, 2019, vol. 12, issue 5, 1-18

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 m 3 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.

Keywords: data center; heat reuse; Computational Fluid Dynamics; prediction algorithm; neural networks (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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