Improving Energy Efficiency in Buildings Using Machine Intelligence

  • Javier Sedano
  • José Ramón Villar
  • Leticia Curiel
  • Enrique de la Cal
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


Improving the detection of thermal insulation in buildings –which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings.


Feature Selection Heating System Machine Intelligence Improve Energy Efficiency Indoor Temperature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J., Lua, L., Yang, H.: Investigation on the thermal performance of different lightweight roofing structures and its effect on space cooling load. Applied Thermal Engineering 29(11-12), 2491–2499 (2009)CrossRefGoogle Scholar
  2. 2.
    Yu, J., Yang, C., Tian, L., Liao, D.: Evaluation on energy and thermal performance for residential envelopes in hot summer and cold winter zone of China. Applied Energy 86(10), 1970–1985 (2009)CrossRefGoogle Scholar
  3. 3.
    Villar, J.R., de la Cal, E., Sedano, J.: A Fuzzy Logic Based Efficient Energy Saving Approach for Domestic Heating Systems. Integrated Computer Aided Engineering 16(2), 151–163 (2009)Google Scholar
  4. 4.
    Villar, J.R., de la Cal, E., Sedano, J.: Minimizing energy consumption in heating systems under uncertainty. In: Corchado, E., Abraham, A., Pedrycz, W. (eds.) HAIS 2008. LNCS (LNAI), vol. 5271, pp. 583–590. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    De la Cal, E., Villar, J.R., Sedano, J.: A thermodynamical model study for an energy saving algorithm. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 384–390. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Lewis, P.T., Alexander, D.K.: Htb2: A flexible model for dynamic building simulation. Building and Environment 1, 7–16 (1990)CrossRefGoogle Scholar
  7. 7.
    Corchado, E., Fyfe, C.: Connectionist Techniques for the Identification and Suppression of Interfering Underlying Factors. Int. Journal of Pattern Recognition and Artificial Intelligence 17(8), 1447–1466 (2003)CrossRefGoogle Scholar
  8. 8.
    Friedman, J.H., Tukey, J.W.: Projection Pursuit Algorithm for Exploratory Data-Analysis. IEEE Transactions on Computers 23(9), 881–890 (1974)CrossRefzbMATHGoogle Scholar
  9. 9.
    Diaconis, P., Freedman, D.: Asymptotics of Graphical Projections. The Annals of Statistics 12(3), 793–815 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery 8(3), 203–225 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. In: Advances in Neural Information Processing Systems, vol. 10, pp. 350–356 (1998)Google Scholar
  12. 12.
    Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: Proc. of the 10th European Symposium on Artificial Neural Networks (ESANN 2002), pp. 143–148 (2002)Google Scholar
  13. 13.
    Corchado, E., Han, Y., Fyfe, C.: Structuring Global Responses of Local Filters Using Lateral Connections. Journal of Experimental & Theoretical Artificial Intelligence 15(4), 473–487 (2003)CrossRefzbMATHGoogle Scholar
  14. 14.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research, Special Issue on variable and Feature Selection 3, 1157–1182 (2003)zbMATHGoogle Scholar
  15. 15.
    Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on IEEE Knowledge and Data Engineering 17(4), 491–502 (2005)CrossRefGoogle Scholar
  16. 16.
    Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2(6), 559–572 (1901)CrossRefzbMATHGoogle Scholar
  17. 17.
    Hotelling, H.: Analysis of a Complex of Statistical Variables Into Principal Components. Journal of Education Psychology 24, 417–444 (1933)CrossRefzbMATHGoogle Scholar
  18. 18.
    Ljung, L.: System Identification, Theory for the User, 2nd edn. Prentice-Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  19. 19.
    Cybenko, G.: Aproximation by superpositions of sigmoidal function. Math. Control, Sygnals and System 2(4), 473–487, 303–314 (1989)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Aproximators. Neural Networks 2(5), 359–366 (1989)CrossRefGoogle Scholar
  21. 21.
    Hansen, L.K., Pedersen, M.W.: Controlled Growth of Cascade Correlation Nets. In: Marinaro, M., Morasso, P.G. (eds.) Proc. ICANN 1994, Sorrento, Italia, pp. 797–800 (1994)Google Scholar
  22. 22.
    Hassibi, B., Stork, D.G.: Second Order Derivatives for Network Pruning: Optimal Brain Surgeon. In: Hanson, S.J., et al. (eds.) Proceedings of the 1992 Conference on Advances in neural Information Processing System 5, pp. 164–171. Morgan Kaufmann, San Mateo (1993)Google Scholar
  23. 23.
    Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley & Sons, Chichester (1987)zbMATHGoogle Scholar
  24. 24.
    Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley, Reading (1991)Google Scholar
  25. 25.
    Nögaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)CrossRefGoogle Scholar
  26. 26.
    Söderström, T., Stoica, P.: System identification. Prentice Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  27. 27.
    Nørgaard, M.: Neural network Based System Identification Toolbox, Report Technical Tecnico. 00-E-891, Department de Automation Technical University of Denmark (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Javier Sedano
    • 1
  • José Ramón Villar
    • 2
  • Leticia Curiel
    • 3
  • Enrique de la Cal
    • 2
  • Emilio Corchado
    • 3
  1. 1.Department of Electromechanical EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of Computer ScienceUniversity of OviedoSpain
  3. 3.Department of Civil EngineeringUniversity of BurgosBurgosSpain

Personalised recommendations