Bioclimatic House Heat Exchanger Behavior Prediction with Time Series Modeling

  • Bruno Baruque
  • Esteban Jove
  • José Luis Casteleiro-Roca
  • Santiago Porras
  • José Luis Calvo-Rolle
  • Emilio Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 649)


The Heat Pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is one of the most representative elements when a heat pump is employed as building heating system. In the present study, a novel intelligent system was designed to predict the performance of on this kind of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements along one year. It was based on time series modeling. Then, the model was validated and verified; it obtains good results in all the operating conditions ranges.


Time series modeling Time delay neural networks Heat exchanger Heat pump Geothermal exchanger 



We would like to thank the ‘Instituto Enerxético de Galicia’ (INEGA) and ‘Parque Eólico Experimental de Sotavento’ (Sotavento Foundation) for their technical support on this work.


  1. 1.
    Porter, D.: Comprehensive Renewable Energy. Elsevier, Oxford (2012)Google Scholar
  2. 2.
    Kaltschmitt, M., Streicher, W., Wiese, A.: Renewable Energy. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Jenssen, T.: Glances at Renewable and Sustainable Energy. Springer, London (2013)CrossRefGoogle Scholar
  4. 4.
    Langley, B.C.: Heat Pump Technology. Prentice Hall PTR, Englewood Cliffs (2002)Google Scholar
  5. 5.
    Casteleiro-Roca, J., Calvo-Rolle, J., Meizoso-Lopez, M., Piñón-Pazos, A., Rodríguez-Gómez, B.: New approach for the QCM sensors characterization. Sens. Actuators A Phys. 207, 1–9 (2014)CrossRefGoogle Scholar
  6. 6.
    Sauer, H., Howell, R.: Heat Pump Systems. Krieger Publishing Company, Malabar (1991)Google Scholar
  7. 7.
    Kakaç, S., Liu, H., Pramuanjaroenkij, A.: Heat Exchangers: Selection, Rating, and Thermal Design, 2nd edn. Taylor & Francis, Philadelphia (2002). Designing for heat transferMATHGoogle Scholar
  8. 8.
    Casteleiro-Roca, J., Calvo-Rolle, J., Meizoso-López, M., Pión-Pazos, A., Rodríguez-Gómez, B.: Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150(Part A), 90–98 (2015)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Cui, P., Li, X., Man, Y., Fang, Z.: Heat transfer analysis of pile geothermal heat exchangers with spiral coils. Appl. Energy 88(11), 4113–4119 (2011)CrossRefGoogle Scholar
  11. 11.
    Calvo-Rolle, J.L., Corchado, E.: A bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)CrossRefGoogle Scholar
  12. 12.
    Calvo-Rolle, J.L., Corchado, E.: A bio-inspired robust controller for a refinery plant process. Logic J. IGPL 20(3), 598–616 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Jove, E., Aláiz-Moretón, H., Casteleiro-Roca, J.L., Corchado, E., Calvo-Rolle, J.L.: Modeling of bicomponent mixing system used in the manufacture of wind generator blades, pp. 275–285. Springer, Cham (2014)Google Scholar
  14. 14.
    Calvo-Rolle, J.L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R.F.: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J. Appl. Logic 13(1), 37–47 (2015)CrossRefGoogle Scholar
  15. 15.
    Casteleiro-Roca, J.L., Pérez, J.A.M., Piñón-Pazos, A.J., Calvo-Rolle, J.L., Corchado, E.: Modeling the electromyogram (EMG) of patients undergoing anesthesia during surgery. In: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 273–283. Springer (2015)Google Scholar
  16. 16.
    Casteleiro-Roca, J.L., Calvo-Rolle, J.L., Méndez Pérez, J.A., Roqueñí Gutiérrez, N., de Cos Juez, F.J.: Hybrid intelligent system to perform fault detection on bis sensor during surgeries. Sensors 17(1), 179 (2017)CrossRefGoogle Scholar
  17. 17.
    Quintián, H., Calvo-Rolle, J.L., Corchado, E.: A hybrid regression system based on local models for solar energy prediction. Informatica 25(2), 265–282 (2014)CrossRefGoogle Scholar
  18. 18.
    Casteleiro-Roca, J.L., Quintián, H., Calvo-Rolle, J.L., Corchado, E., del Carmen Meizoso-López, M., Piñón-Pazos, A.: An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger. J. Appl. Logic 17, 36–47 (2016)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Quintián, H., Casteleiro-Roca, J.L., Perez-Castelo, F.J., Calvo-Rolle, J.L., Corchado, E.: Hybrid intelligent model for fault detection of a lithium iron phosphate power cell used in electric vehicles. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 751–762. Springer (2016)Google Scholar
  20. 20.
    Alaiz Moretn, H., Calvo-Rolle, J.L., Garca, I., Alonso Alvarez, A.: Formalization and practical implementation of a conceptual model for PID controller tuning. Asian J. Control 13(6), 773–784 (2011)CrossRefMATHGoogle Scholar
  21. 21.
    Quintian Pardo, H., Calvo Rolle, J.L., Fontenla Romero, O.: Application of a low cost commercial robot in tasks of tracking of objects. Dyna 79(175), 24–33 (2012)Google Scholar
  22. 22.
    Corchado, E., Abraham, A., Snasel, V.: New trends on soft computing models in industrial and environmental applications. Neurocomputing 109, 1–2 (2013)CrossRefGoogle Scholar
  23. 23.
    Kang, J., Meng, W., Abraham, A., Liu, H.: An adaptive PID neural network for complex nonlinear system control. Neurocomputing 135, 79–85 (2014)CrossRefGoogle Scholar
  24. 24.
    Machón-González, I., López-García, H., Calvo-Rolle, J.L.: A hybrid batch SOM-NG algorithm. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE (2010)Google Scholar
  25. 25.
    Crespo-Ramos, M.J., Machón-González, I., López-García, H., Calvo-Rolle, J.L.: Detection of locally relevant variables using som-ng algorithm. Eng. Appl. Artif. Intell. 26(8), 1992–2000 (2013)CrossRefGoogle Scholar
  26. 26.
    Garcia, R.F., Rolle, J.L.C., Gomez, M.R., Catoira, A.D.: Expert condition monitoring on hydrostatic self-levitating bearings. Expert Syst. Appl. 40(8), 2975–2984 (2013)CrossRefGoogle Scholar
  27. 27.
    Calvo-Rolle, J.L., Machón-González, I., López-García, H.: Neuro-robust controller for non-linear systems. Dyna 86(3), 308–317 (2011)CrossRefGoogle Scholar
  28. 28.
    Calvo-Rolle, J.L., Fontenla-Romero, O., Pérez-Sánchez, B., Guijarro-Berdiñas, B.: Adaptive inverse control using an online learning algorithm for neural networks. Informatica 25(3), 401–414 (2014)CrossRefGoogle Scholar
  29. 29.
    Wojnowicz, M., Chisholm, G., Wallace, B., Wolff, M., Zhao, X., Luan, J.: Suspend: Determining software suspiciousness by non-stationary time series modeling of entropy signals. Expert Syst. Appl. 71, 301–318 (2017)CrossRefGoogle Scholar
  30. 30.
    Peng, H., Kitagawa, G., Tamura, Y., Xi, Y., Qin, Y., Chen, X.: A modeling approach to financial time series based on market microstructure model with jumps. Appl. Soft Comput. 29, 40–51 (2015)CrossRefGoogle Scholar
  31. 31.
    Wohler, C., Anlauf, J.K.: An adaptable time-delay neural-network algorithm for image sequence analysis. IEEE Trans. Neural Netw. 10(6), 1531–1536 (1999)CrossRefGoogle Scholar
  32. 32.
    Peddinti, V., Povey, D., Khudanpur, S.: A time delay neural network architecture for efficient modeling of long temporal contexts. In: INTERSPEECH, pp. 3214–3218 (2015)Google Scholar
  33. 33.
    Gupta, A.: Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications, A. K. Palit, and D. Popovic, 362 pp. Springer, London (2005). ISBN: 1852339489. (International Journal of Robust and Nonlinear Control 17(4), 351–354 (2007))Google Scholar
  34. 34.
    Bontempi, G., Ben Taieb, S., Borgne, Y.-A.: Machine learning strategies for time series forecasting, pp. 62–77. Springer, Heidelberg (2013)Google Scholar
  35. 35.
    Menezes, Jr., J.M.P., Barreto, G.A.: Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing 71(16–18), 3335–3343 (2008). Advances in Neural Information Processing (ICONIP 2006)/Brazilian Symposium on Neural Networks (SBRN 2006)Google Scholar
  36. 36.
    Pisoni, E., Farina, M., Carnevale, C., Piroddi, L.: Forecasting peak air pollution levels using NARX models. Eng. Appl. Artif. Intell. 22(4–5), 593–602 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Bruno Baruque
    • 1
  • Esteban Jove
    • 2
  • José Luis Casteleiro-Roca
    • 2
  • Santiago Porras
    • 3
  • José Luis Calvo-Rolle
    • 2
  • Emilio Corchado
    • 4
  1. 1.Departamento de Ingeniería CivilUniversity of BurgosBurgosSpain
  2. 2.Departamento de Ingeniería IndustrialUniversity of A CoruñaFerrolSpain
  3. 3.Departamento de Economía AplicadaUniversity of BurgosBurgosSpain
  4. 4.Departamento de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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