IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p814-d210045.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/814/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/814/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Yuan, Xiaolei & Liang, Yumin & Hu, Xinyi & Xu, Yizhe & Chen, Yongbao & Kosonen, Risto, 2023. "Waste heat recoveries in data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. 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).
    5. Guelpa, Elisa, 2020. "Impact of network modelling in the analysis of district heating systems," Energy, Elsevier, vol. 213(C).
    6. 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.
    7. Putna, Ondřej & Janošťák, František & Šomplák, Radovan & Pavlas, Martin, 2018. "Demand modelling in district heating systems within the conceptual design of a waste-to-energy plant," Energy, Elsevier, vol. 163(C), pages 1125-1139.
    8. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    9. Rämä, Miika & Wahlroos, Mikko, 2018. "Introduction of new decentralised renewable heat supply in an existing district heating system," Energy, Elsevier, vol. 154(C), pages 68-79.
    10. Li, Weiwei & Qian, Tong & Zhao, Wei & Huang, Wenwei & Zhang, Yin & Xie, Xuehua & Tang, Wenhu, 2023. "Decentralized optimization for integrated electricity–heat systems with data center based energy hub considering communication packet loss," Applied Energy, Elsevier, vol. 350(C).
    11. Johan Simonsson & Khalid Tourkey Atta & Gerald Schweiger & Wolfgang Birk, 2021. "Experiences from City-Scale Simulation of Thermal Grids," Resources, MDPI, vol. 10(2), pages 1-20, January.
    12. Francesco Neirotti & Michel Noussan & Stefano Riverso & Giorgio Manganini, 2019. "Analysis of Different Strategies for Lowering the Operation Temperature in Existing District Heating Networks," Energies, MDPI, vol. 12(2), pages 1-17, January.
    13. Pauli Hiltunen & Sanna Syri, 2020. "Highly Renewable District Heat for Espoo Utilizing Waste Heat Sources," Energies, MDPI, vol. 13(14), pages 1-21, July.
    14. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    15. Jiawen Yu & Yanqiu Yan & Yiqiang Jiang & Jie Ge, 2022. "Renewable energy configuration scheme of data center in cold area. A case study [An overview of renewable energy resources and grid integration for commercial building applications]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 411-420.
    16. Zhang, Youjun & Xiong, Nian & Ge, Zhihua & Zhang, Yichen & Hao, Junhong & Yang, Zhiping, 2020. "A novel cascade heating system for waste heat recovery in the combined heat and power plant integrating with the steam jet pump," Applied Energy, Elsevier, vol. 278(C).
    17. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    18. Jacek Kropiwnicki & Mariusz Furmanek & Andrzej Rogala, 2021. "Modular Approach for Modelling Warming up Process in Water Installations with Flow-Regulating Elements," Energies, MDPI, vol. 14(15), pages 1-17, July.
    19. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu & Shen, Boyang, 2022. "A 10 MW class data center with ultra-dense high-efficiency energy distribution: Design and economic evaluation of superconducting DC busbar networks," Energy, Elsevier, vol. 250(C).
    20. Peng Yin & Yang Guo & Man Zhang & Jiaqiang Wang & Linfeng Zhang & Da Feng & Weike Ding, 2024. "Performance Analysis of Lake Water Cooling Coupled with a Waste Heat Recovery System in the Data Center," Sustainability, MDPI, vol. 16(15), pages 1-21, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:814-:d:210045. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.