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Bioclimatic House Heat Exchanger Behavior Prediction with Time Series Modeling

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International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding (SOCO 2017, ICEUTE 2017, CISIS 2017)

Abstract

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.

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References

  1. Porter, D.: Comprehensive Renewable Energy. Elsevier, Oxford (2012)

    Google Scholar 

  2. Kaltschmitt, M., Streicher, W., Wiese, A.: Renewable Energy. Springer, Heidelberg (2007)

    Book  Google Scholar 

  3. Jenssen, T.: Glances at Renewable and Sustainable Energy. Springer, London (2013)

    Book  Google Scholar 

  4. Langley, B.C.: Heat Pump Technology. Prentice Hall PTR, Englewood Cliffs (2002)

    Google Scholar 

  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)

    Article  Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  11. Calvo-Rolle, J.L., Corchado, E.: A bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  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. Corchado, E., Abraham, A., Snasel, V.: New trends on soft computing models in industrial and environmental applications. Neurocomputing 109, 1–2 (2013)

    Article  Google Scholar 

  23. Kang, J., Meng, W., Abraham, A., Liu, H.: An adaptive PID neural network for complex nonlinear system control. Neurocomputing 135, 79–85 (2014)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  27. Calvo-Rolle, J.L., Machón-González, I., López-García, H.: Neuro-robust controller for non-linear systems. Dyna 86(3), 308–317 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Bontempi, G., Ben Taieb, S., Borgne, Y.-A.: Machine learning strategies for time series forecasting, pp. 62–77. Springer, Heidelberg (2013)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

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Acknowledgments

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.

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Correspondence to Esteban Jove .

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Baruque, B., Jove, E., Casteleiro-Roca, J.L., Porras, S., Calvo-Rolle, J.L., Corchado, E. (2018). Bioclimatic House Heat Exchanger Behavior Prediction with Time Series Modeling. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-67180-2_11

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