Modelling of Heat Flux in Building Using Soft-Computing Techniques
Improving the detection of thermal insulation failures in buildings includes the development of models for heating process and fabric gain -heat flux through exterior walls in the building-. Thermal insulation standards are now contractual obligations in new buildings, the energy efficiency in the case of buildings constructed before the regulations adopted is still an open issue, and the assumption is that it will be based on heat flux and conductivity measurement. A three-step procedure is proposed in this study 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 modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, a supervised neural model and identification techniques are applied, in order to detect the heat flux through exterior walls in the building. The reliability of the proposed method is validated for a winter zone, associated to several cities in Spain.
KeywordsComputational Intelligence Soft computing Systems Identification Systems Artificial Neural Networks Non-linear Systems
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- 7.Corchado, E., Fyfe, C.: Orientation selection using Maximum Likelihood Hebbian learning. International Journal of Knowledge-Based Intelligent Engineering 7(2) (2003)Google Scholar
- 8.Directive 2002/91/CE of the European Parliament and the Council of 16 December 2002 on the energy performance of buildings. Official Journal of the European Community (2003)Google Scholar
- 9.Real Decreto 314/2006, de 17 de Marzo. BOE núm 74, Reino de España (2006)Google Scholar
- 10.Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2(6), 559–572 (1901)Google Scholar
- 12.Seung, H.S., Socci, N.D., Lee, D.: The rectified Gaussian distribution. Advances in Neural Information Processing Systems 10, 350–356 (1998)Google Scholar
- 13.Fyfe, C., Corchado, E.: Maximum likelihood Hebbian rules. In: Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN 2002), pp. 143–148. D-side Publishers, Bruges (2002)Google Scholar
- 15.Ljung, L.: System Identification. Theory for the User, 2nd edn. Prentice-Hall, Upper Saddle River (1999)Google Scholar
- 18.Nørgaard. M.: Neural network based system identification toolbox. Technical Report 00-E-891, Dept. of Automation, Technical University of Denmark (2000)Google Scholar