A Neural Classifier for the Optimal Selection of Conduction Transfer Functions

摘要：

The transfer function method (TFM) is a very spread approach for the solution of heat transfer problems in building envelopes.However,the reliability of the simulation can be significantly affected by the choice of the set of coefficients for the conduction transfer functions (CTFs),especially in presence of very massive walls.This paper discusses a neural network classifier able to assess the reliability of CTFs on the basis of a specific evaluation parameter based on a comparison with the result obtained by the Fourier analysis.The model,trained with several thousands of samples obtained by using a software created by the authors,performs very well,as it is showed by the confusion matrix presented in the paper.It represents a very useful tool for the HVAC plants designers because it allows the selection of the best CTFs for the computation of the heat flows though multilayer walls before running simulations.

关键词： Z-transform Neural classifier Conduction transfer function

作者: Giorgio Beccali Maurizio Cellura Valerio Lo Brano Antonino Marvuglia Aldo Orioli

作者单位: Dipartimento di Ricerche Energetiche ed Ambientali,Palermo University,Palermo,Italy

会议类型: 国际会议

会议名称: The First International Conference on Building Energy and Environment(第一届建筑能源与环境国际会议)

会议地点: 大连

会议语种:英文

页码: 1872-1879

在线出版日期: 2008-07-01（万方平台首次上网日期，不代表论文的发表时间）