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 / M. Tosun in International journal of refrigeration, Vol. 34 N° 1 (Janvier 2011)  

Public ISBD [article]  inInternational journal of refrigeration > Vol. 34 N° 1 (Janvier 2011) . - pp. 362-373 Titre : 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 Titre original : 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 Type de document : texte imprimé Auteurs : M. Tosun, Auteur ; K. Dincer, Auteur Année de publication : 2011 Article en page(s) : pp. 362-373 Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Insulation Thermal analysis Wall Building Cooling Neural network Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération 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 (di) 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. As ANN input parameters, Uw, Te,Met, Te,TSE, Rwt, and qTSE were used, while di was the output parameter. 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 modeling method of di studies. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700710001738 [article] 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 = 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 [texte imprimé] / M. Tosun, Auteur ; K. Dincer, Auteur . - 2011 . - pp. 362-373. Génie Mécanique Langues : Anglais (eng) in International journal of refrigeration > Vol. 34 N° 1 (Janvier 2011) . - pp. 362-373 Mots-clés : Insulation Thermal analysis Wall Building Cooling Neural network Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération 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 (di) 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. As ANN input parameters, Uw, Te,Met, Te,TSE, Rwt, and qTSE were used, while di was the output parameter. 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 modeling method of di studies. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700710001738

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