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

Original Article


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.


Neural networks Indoor relative humidity prediction Indoor temperature prediction NNARX model Genetic algorithm Model validation 


  1. 1.
    Reijula K (2004) Moisture-problem buildings with molds causing work-related diseases. Adv Appl Microbiol 55:175–189CrossRefGoogle Scholar
  2. 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 22:381–385CrossRefGoogle Scholar
  3. 3.
    Lu X (2002) Modelling heat and moisture transfer in buildings—(I) model program. Energy Build 34:1033–1043CrossRefGoogle Scholar
  4. 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):655–664CrossRefGoogle Scholar
  5. 5.
    Ruano AE, Crispim EM, Conceição EZE, Lúcio MMJR (2006) Prediction of building’s temperature using neural networks models. Energy Build 38:682–694CrossRefGoogle Scholar
  6. 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 1297–1301Google Scholar
  7. 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 63–70Google Scholar
  8. 8.
    Ferreira PM, Faria EA, Ruano AE (2002) Neural network models in greenhouse air temperature prediction. Neurocomputing 43:51–75MATHCrossRefGoogle Scholar
  9. 9.
    Thomas B, Soleimani-Mosheni M (2007) Artificial neural network models for indoor temperature prediction: investigations in two buildings. Neural Comput Appl 16:81–89Google Scholar
  10. 10.
    Nørgaard M, Rvan O, Poulsen NK, Hansen LK (2000) Neural networks for modelling and control of dynamic systems. Springer, LondonGoogle Scholar
  11. 11.
    Tibshirani R (1996) A comparison of some error estimates for neural network models. Neural Comput 8:152–163CrossRefGoogle Scholar
  12. 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 176–182Google Scholar
  13. 13.
    Chen S, Billings SA, Cowan CFN, Grant PM (1990) Practical identification of Narmax models using radial basis functions. Int J Control 52:1327–1350MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Irie B, Miyaki S (1988) Capabilities of three layer perceptrons. In: Proceedings of the IEEE Second International Conference on Neural Networks, San Diego, CAGoogle Scholar
  15. 15.
    Demuth H, Beal M (1988) Neural network toolbox user’s guide. Version 3.0. The Math Works, Inc. NatickGoogle Scholar
  16. 16.
    Bloem H (1993) Workshop on system identification applied to building performance data. Institute for systems engineering and informatics, Joint research centre, Ispra, ItalyGoogle Scholar
  17. 17.
    Levenberg K (1944) A method for the solution of certain problems in least squares. Q Appl Math 2:164–168MATHMathSciNetGoogle Scholar
  18. 18.
    Marquard D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441CrossRefGoogle Scholar
  19. 19.
    Nørgaard M (2000) Neural network based system identification toolbox. Tech. Report. 00-e 891, Department of Automation Technical University of DenmarkGoogle Scholar
  20. 20.
    Hansen LK, Larsen J (1996) Linear unlearning for cross-validation. Adv Comput Math 5:269–280MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, New YorkMATHGoogle Scholar
  22. 22.
    De Veanux RD, Schumi J, Schweinsberg J, Ungar LH (1998) Prediction intervals for neural networks via nonlinear regression. Technometrics 40(4):273–282CrossRefMathSciNetGoogle Scholar
  23. 23.
    Bishop CM (1995) Neural Networks for pattern Recognition. Clarendon Press, OxfordGoogle Scholar
  24. 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 1–37Google Scholar
  25. 25.
    Sharkey AJC (1999) Combining artificial neural nets: ensemble and modular multi-net systems. Springer, BerlinMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Laboratory of Structural Engineering and Building Physics, Department of Civil and Environmental EngineeringHelsinki University of TechnologyEspooFinland

Personalised recommendations