Aksaray University Journal of Science and Engineering

Abstract

In this study, two different artificial neural network models were used
for insulation and non-insulation of the heating pipes used for heating in
buildings and two different artificial neural networks (YSA) models for the
insulated and non-insulated states of the building walls. 3-layer forward feed
in YSA models designed for these situations and a back-propagation model is
preferred. The sigmoid transfer function is used in the hidden layer and the
linear transfer function is used in the output layer. Back propagation
artificial neural network topology is preferred as YSA model and the data were
presented to the network in normalized form. The temperature values obtained
from the network are compared with the measured temperature values and the
results are very close to one another. In this way, the use of artificial
neural network method for estimation of 4 different internal models, definition
of models and the prediction power has increased. In the random and periodic
time interval, the inner plaster thickness is 2 cm, the outer plaster thickness
is 3 cm and according to the wall width of 17 cm, 10 cm thick insulation (xps
material insulated) and according to the non-insulated wall parameters The
statistical data generated from this table that is not based on a nonlinear
formula, ie, YSA, is introduced to the network structure and the results
obtained by testing from the YSA model in the Matlab environment after training
were compared and values very close to each other were determined. Again, in a
random and periodic time interval insulated with 100 mm pipe size (insulated
stapler material) and the values obtained from the table according to the
uninsulated pipe parameters and the results from the YSA model were compared
and compared very close values have been determined.