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Modélisation d'un système d'isolation thermique fondée sur les températures les plus froides, à l'aide de réseaux neuronaux artificiels, afin de déterminer la performance de plusieurs types de murs d'immeubles en Turquie.

Modelling of a thermal insulation system based on the coldest temperature conditions by using artificial neural networks to determine performance of building for wall types in Turkey.

Auteurs : TOSUN M., DINCER K.

Type d'article : Article, Article de la RIF

Résumé

In formation of building external envelope, as two important criteria, climatic data and wall types must be taken into consideration. In the selection of wall type, the thickness of thermal insulation layer must be calculated. As a new approach, this study proposes determining the thermal insulation layer by using artificial neural network (ANN) technique. In this technique five different wall types in four different climatic regions in Turkey have been selected. The ANN was trained and tested by using MATLAB toolbox on a personal computer. It was found that the maximum mean absolute percentage error (MRE, %) is less than 7.658%. R2 (%) for the training data were found ranging about from 99.68 to 99.98 and R2 for the testing data varied between 97.55 and 99.96. These results show that ANN model can be used as a reliable modelling method of thermal insulation layer studies.

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Format PDF

Pages : pp. 362-373

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  • Prix public

    20 €

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    Gratuit

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Détails

  • Titre original : Modelling of a thermal insulation system based on the coldest temperature conditions by using artificial neural networks to determine performance of building for wall types in Turkey.
  • Identifiant de la fiche : 2010-1542
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 34 - n. 1
  • Date d'édition : 01/2011

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