This paper presents a new mathematical approach applied to the Conduction Transfer Functions (CTFs) of a multilayered wall to predict the reliability of building simulations based upon them. Such a procedure can be used to develop a decision support system that identifies the best condition to calculate the best CTFs set. This is a critical point at the core of ASHRAE calculation methodology founded on the Transfer Function Method (TFM). To evaluate the performance of different CTFs sets, the authors built a large amount of data, subsequently employed to train a Neural Network Classifier (NNC) able to predict the reliability of a simulation without performing it. For this purpose all the multilayered walls included in the HVAC ASHRAE Handbook were used, and moreover many other walls typical of Mediterranean building heritage were added. The results show that the proposed method to optimize CTFs based on NNC is highly accurate, fast and easy to integrate in other buildings simulation tools.

To assess the validity of the transfer function method: A neural model for the optimal choice of conduction transfer functions

MISTRETTA, Marina;
2010

Abstract

This paper presents a new mathematical approach applied to the Conduction Transfer Functions (CTFs) of a multilayered wall to predict the reliability of building simulations based upon them. Such a procedure can be used to develop a decision support system that identifies the best condition to calculate the best CTFs set. This is a critical point at the core of ASHRAE calculation methodology founded on the Transfer Function Method (TFM). To evaluate the performance of different CTFs sets, the authors built a large amount of data, subsequently employed to train a Neural Network Classifier (NNC) able to predict the reliability of a simulation without performing it. For this purpose all the multilayered walls included in the HVAC ASHRAE Handbook were used, and moreover many other walls typical of Mediterranean building heritage were added. The results show that the proposed method to optimize CTFs based on NNC is highly accurate, fast and easy to integrate in other buildings simulation tools.
Building simulation; Conduction transfer functions; Neural models; Mathematical approach; Neural network classifier; Simulation tool
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/14776
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