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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Marcel Antal & Tudor Cioara & Ionut Anghel & Radoslaw Gorzenski & Radoslaw Januszewski & Ariel Oleksiak & Wojciech Piatek & Claudia Pop & Ioan Salomie & Wojciech Szeliga, 2019. "Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model," Energies, MDPI, vol. 12(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:814-:d:210045
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    References listed on IDEAS

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    1. Marcel Antal & Tudor Cioara & Ionut Anghel & Claudia Pop & Ioan Salomie, 2018. "Transforming Data Centers in Active Thermal Energy Players in Nearby Neighborhoods," Sustainability, MDPI, vol. 10(4), pages 1-20, March.
    2. Linas Gelažanskas & Kelum A. A. Gamage, 2015. "Forecasting Hot Water Consumption in Residential Houses," Energies, MDPI, vol. 8(11), pages 1-16, November.
    3. Wahlroos, Mikko & Pärssinen, Matti & Manner, Jukka & Syri, Sanna, 2017. "Utilizing data center waste heat in district heating – Impacts on energy efficiency and prospects for low-temperature district heating networks," Energy, Elsevier, vol. 140(P1), pages 1228-1238.
    4. Jing Ni & Bowen Jin & Bo Zhang & Xiaowei Wang, 2017. "Simulation of Thermal Distribution and Airflow for Efficient Energy Consumption in a Small Data Centers," Sustainability, MDPI, vol. 9(4), pages 1-16, April.
    5. del Hoyo Arce, Itzal & Herrero López, Saioa & López Perez, Susana & Rämä, Miika & Klobut, Krzysztof & Febres, Jesus A., 2018. "Models for fast modelling of district heating and cooling networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P2), pages 1863-1873.
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    Cited by:

    1. Vesterlund, Mattias & Borisová, Stanislava & Emilsson, Ellinor, 2024. "Data center excess heat for mealworm farming, an applied analysis for sustainable protein production," Applied Energy, Elsevier, vol. 353(PA).
    2. Birol Kılkış & Malik Çağlar & Mert Şengül, 2021. "Energy Benefits of Heat Pipe Technology for Achieving 100% Renewable Heating and Cooling for Fifth-Generation, Low-Temperature District Heating Systems," Energies, MDPI, vol. 14(17), pages 1-54, August.
    3. Cristina Ramos Cáceres & Suzanna Törnroth & Mattias Vesterlund & Andreas Johansson & Marcus Sandberg, 2022. "Data-Center Farming: Exploring the Potential of Industrial Symbiosis in a Subarctic Region," Sustainability, MDPI, vol. 14(5), pages 1-23, February.
    4. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    5. Tudor Cioara & Marcel Antal & Claudia Daniela Antal (Pop) & Ionut Anghel & Massimo Bertoncini & Diego Arnone & Marilena Lazzaro & Marzia Mammina & Terpsichori-Helen Velivassaki & Artemis Voulkidis & Y, 2020. "Data Centers Optimized Integration with Multi-Energy Grids: Test Cases and Results in Operational Environment," Sustainability, MDPI, vol. 12(23), pages 1-23, November.

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