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Prediction of indoor temperature and relative humidity using neural network models: model comparison

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Abstract

The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.

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References

  1. Reijula K (2004) Moisture-problem buildings with molds causing work-related diseases. Adv Appl Microbiol 55:175–189

    Article  Google Scholar 

  2. Luosujarvi R, Husman T, Seuri M, Pietikainen M, Pollari P, Pelkonen J, Hujakka H, Kaipiainen-Seppanen O, Aho K (2003) Joint symptoms and diseases associated with moisture damage in a health center. Clin Rheumatol 22:381–385

    Article  Google Scholar 

  3. Lu X (2002) Modelling heat and moisture transfer in buildings—(I) model program. Energy Build 34:1033–1043

    Article  Google Scholar 

  4. Teodoisu C, Hohota R, Rusaouën G, Woloszyn M (2003) Numerical prediction of indoor air humidity and its effect on indoor environment. Build Environ 38(5):655–664

    Article  Google Scholar 

  5. Ruano AE, Crispim EM, Conceição EZE, Lúcio MMJR (2006) Prediction of building’s temperature using neural networks models. Energy Build 38:682–694

    Article  Google Scholar 

  6. Sigumonrong AP, Bong TY, Fok SC, Wong YW (2001) Self-learning neurocontroller for maintaining indoor relative humidity. In: Proceedings of the International Joint Conference on Neural Networks v2, IEEE, Washington, DC, USA, pp 1297–1301

  7. Zhang Q, Wong YW, Fok SC, Bong TY (2005) Neural-based air-handling unit for indoor relative humidity and temperature control. In: ASHRAE Transactions v 111 PART 1—Technical and Symposium Papers presented at the 2005 Winter Meeting of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE, Orlando, FL, USA, pp 63–70

  8. Ferreira PM, Faria EA, Ruano AE (2002) Neural network models in greenhouse air temperature prediction. Neurocomputing 43:51–75

    Article  MATH  Google Scholar 

  9. Thomas B, Soleimani-Mosheni M (2007) Artificial neural network models for indoor temperature prediction: investigations in two buildings. Neural Comput Appl 16:81–89

    Google Scholar 

  10. Nørgaard M, Rvan O, Poulsen NK, Hansen LK (2000) Neural networks for modelling and control of dynamic systems. Springer, London

    Google Scholar 

  11. Tibshirani R (1996) A comparison of some error estimates for neural network models. Neural Comput 8:152–163

    Article  Google Scholar 

  12. Heskes T (1997) Practical confidence and prediction intervals. In: Mozer M, Jordan M, Pekes T (eds) Advances in neural information processing system 9. MIT Press, Cambridge, pp 176–182

    Google Scholar 

  13. Chen S, Billings SA, Cowan CFN, Grant PM (1990) Practical identification of Narmax models using radial basis functions. Int J Control 52:1327–1350

    Article  MATH  MathSciNet  Google Scholar 

  14. Irie B, Miyaki S (1988) Capabilities of three layer perceptrons. In: Proceedings of the IEEE Second International Conference on Neural Networks, San Diego, CA

  15. Demuth H, Beal M (1988) Neural network toolbox user’s guide. Version 3.0. The Math Works, Inc. Natick

    Google Scholar 

  16. Bloem H (1993) Workshop on system identification applied to building performance data. Institute for systems engineering and informatics, Joint research centre, Ispra, Italy

  17. Levenberg K (1944) A method for the solution of certain problems in least squares. Q Appl Math 2:164–168

    MATH  MathSciNet  Google Scholar 

  18. Marquard D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441

    Article  Google Scholar 

  19. Nørgaard M (2000) Neural network based system identification toolbox. Tech. Report. 00-e 891, Department of Automation Technical University of Denmark

  20. Hansen LK, Larsen J (1996) Linear unlearning for cross-validation. Adv Comput Math 5:269–280

    Article  MATH  MathSciNet  Google Scholar 

  21. Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, New York

    MATH  Google Scholar 

  22. De Veanux RD, Schumi J, Schweinsberg J, Ungar LH (1998) Prediction intervals for neural networks via nonlinear regression. Technometrics 40(4):273–282

    Article  MathSciNet  Google Scholar 

  23. Bishop CM (1995) Neural Networks for pattern Recognition. Clarendon Press, Oxford

    Google Scholar 

  24. Schaffer JD, Whitley D, Eshelman L (1992) Combination of genetic algorithm and neural networks: A survey of the state of art. In: International workshop on Combinations of Genetic Algorithms and Neural Networks, Baltimore, MD, USA, pp 1–37

  25. Sharkey AJC (1999) Combining artificial neural nets: ensemble and modular multi-net systems. Springer, Berlin

    MATH  Google Scholar 

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Acknowledgments

We are grateful to the Academy of Finland for financial support.

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Correspondence to Tao Lu.

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Lu, T., Viljanen, M. Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Comput & Applic 18, 345–357 (2009). https://doi.org/10.1007/s00521-008-0185-3

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  • DOI: https://doi.org/10.1007/s00521-008-0185-3

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