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Geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique

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Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference (DCAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1173))

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Abstract

The goal of this study was to test many models in order to check which one better predicted the heat exchanger of a bioclimatic building. A number of regression models, pre-processing methods, and data analyses are compared in the study to forecast the input collector temperature of the heat pump. Specifically, three different techniques have been considered in this research work: Multilayer Perceptrons, Long Short Term Memory networks and Convolutional Neural Networks. Satisfactory results have been obtained in all cases for predicting the temperature 24 h in advance, implementing an useful tool for enhance energy management.

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References

  1. Baruque, B., Porras, S., Jove, E., Calvo-Rolle, J.L.: Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization. Energy 171, 49–60 (2019)

    Article  Google Scholar 

  2. Cheker, Z., Chakkor, S., El Oualkadi, A.: Visual evoked potential classification support with convolutional neural network and recurrent neural network-a comparative study. In: 2022 International Conference on Decision Aid Sciences and Applications (DASA), pp. 1612–1617. IEEE (2022). https://doi.org/10.1109/DASA54658.2022.9765034

  3. Dickson, M.H., Fanelli, M.: Geothermal Energy: Utilization and Technology. Routledge (2013)

    Google Scholar 

  4. (EC), E.C.: Europe 2020: A Strategy for Smart, Sustainable and Inclusive Growth. Working Paper (COM 2010) (2010)

    Google Scholar 

  5. Energy, G.: Data page: geothermal energy capacity. (2023). https://ourworldindata.org/grapher/installed-geothermal-capacity. Accessed 20 May 2024

  6. Florides, G., Kalogirou, S.: Ground heat exchangers’a review of systems, models and applications. Renew. Energy 32(15), 2461–2478 (2007). https://doi.org/10.1016/j.renene.2006.12.014

  7. Hassan, Q., et al.: A comprehensive review of international renewable energy growth. Energy Built Environ. (2024)

    Google Scholar 

  8. Idroes, G.M., Hardi, I., Hilal, I.S., Utami, R.T., Noviandy, T.R., Idroes, R.: Economic growth and environmental impact: assessing the role of geothermal energy in developing and developed countries. Innov. Green Developm. 3(3), 100144 (2024)

    Article  Google Scholar 

  9. Igeland, P., Schroeder, L., Yahya, M., Okhrin, Y., Uddin, G.S.: The energy transition: the behavior of renewable energy stock during the times of energy security uncertainty. Renew. Energy 221, 119746 (2024)

    Article  Google Scholar 

  10. Jiang, A., Qin, Z., Faulder, D., Cladouhos, T.T., Jafarpour, B.: Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs. Geothermics 104, 102439 (2022). https://doi.org/10.1016/j.geothermics.2022.102439

  11. Kakaç, S., Liu, H., Pramuanjaroenkij, A.: Heat Exchangers: Selection, Rating, and Thermal Design, Designing for Heat Transfer, 2nd edn. Taylor & Francis(2002)

    Google Scholar 

  12. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1d convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021). https://doi.org/10.1016/j.ymssp.2020.107398

  13. Naser, M., Alavi, A.H.: Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences. Architect. Struct. Construct. 3(4), 499–517 (2023). https://doi.org/10.1007/s44150-021-00015-8

    Article  Google Scholar 

  14. Ozgener, L.: A review on the experimental and analytical analysis of earth to air heat exchanger (EAHE) systems in turkey. Renew. Sustain. Energy Rev. 15(9), 4483–4490 (2011). https://doi.org/10.1016/j.rser.2011.07.103

    Article  Google Scholar 

  15. Ozgener, L., Ozgener, O.: Monitoring of energy exergy efficiencies and exergoeconomic parameters of geothermal district heating systems (GDHSS). Appl. Energy 86(9), 1704–1711 (2009). https://doi.org/10.1016/j.apenergy.2008.11.017

  16. Ozgener, O., Ozgener, L.: Modeling of driveway as a solar collector for improving efficiency of solar assisted geothermal heat pump system: a case study. Renew. Sustain. Energy Rev. 46, 210–217 (2015). https://doi.org/10.1016/j.rser.2015.02.043

  17. Puppala, H., Saikia, P., Kocherlakota, P., Suriapparao, D.V.: Evaluating the applicability of neural network to determine the extractable temperature from a shallow reservoir of puga geothermal field. Int. J. Thermofluids 17, 100259 (2023). https://doi.org/10.1016/j.ijft.2022.100259

  18. Rezaei, A., Kolahdouz, E., Dargush, G., Weber, A.: Ground source heat pump pipe performance with tire derived aggregate. Int. J. Heat Mass Transf. 55(11–12), 2844–2853 (2012). https://doi.org/10.1016/j.ijheatmasstransfer.2012.02.004

    Article  Google Scholar 

  19. Sauer, H., Howell, R.: Heat Pump Systems. Krieger Publishing Company (1991)

    Google Scholar 

  20. Sayed, E.T., Olabi, A.G., Alami, A.H., Radwan, A., Mdallal, A., Rezk, A., Abdelkareem, M.A.: Renewable energy and energy storage systems. Energies 16(3), 1415 (2023)

    Article  Google Scholar 

  21. Sotavento. (2023). https://www.sotaventogalicia.com/area-tecnica/instalaciones-renovables/minieolica/. Accessed 13 Jan 2024

  22. Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intel. Transport Syst. 11(2), 68–75 (2017). https://doi.org/10.1049/iet-its.2016.0208

    Article  Google Scholar 

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Acknowledgments

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project 2022.06822.PTDC (https://doi.org/10.54499/2022.06822.PTDC). The work of Pedro Oliveira was supported by the doctoral Grant PRT/BD/154311/2022 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from European Union, under MIT Portugal Program. CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS). This work has been supported by Centro Mixto de Investigación UDC-NAVANTIA (IN853C 2022/01), funded by GAIN (Xunta de Galicia) and ERDF Galicia 2021-2027.

Author information

Authors and Affiliations

  1. ALGORITMI/LASI Centre, University of Minho, Braga, Portugal

    Pedro Oliveira, Franciso S. Marcondes, Paulo Novais & Esteban Jove

  2. University of A Coruña, Department of Industrial Engineering, CTC, CITIC, Ferrol, A Coruña, Spain

    Paula Arcano-Bea & José Luis Calvo-Rolle

Authors
  1. Pedro Oliveira
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  2. Paula Arcano-Bea
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  3. Franciso S. Marcondes
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  4. José Luis Calvo-Rolle
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  5. Paulo Novais
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  6. Esteban Jove
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Corresponding author

Correspondence to José Luis Calvo-Rolle .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Francisco Zayas-Gato

  2. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Antonio Díaz-Longueira

  3. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    José-Luis Casteleiro-Roca

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Esteban Jove

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Oliveira, P., Arcano-Bea, P., Marcondes, F.S., Calvo-Rolle, J.L., Novais, P., Jove, E. (2024). Geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique. In: Zayas-Gato, F., Díaz-Longueira, A., Casteleiro-Roca, JL., Jove, E. (eds) Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference. DCAI 2024. Lecture Notes in Networks and Systems, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-031-73910-1_5

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