@conference{
author = "Kerčov, Anton and Jovanović, Radiša and Bajc, Tamara",
year = "2023",
abstract = "Compared to methodology provided by standards concerning thermal comfort, by using
models based on various approximation methods or artificial intelligence, it may be possible
to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of
this study is to compare Predicted Mean Vote (PMV) computation model established by using
multiple linear regression and trained artificial neural network, from the standpoint of
accuracy. Both models are established on the basis of the same dataset which consists of 400
combinations of 4 thermal comfort parameters. These parameters are the air temperature,
mean radiant temperature, relative humidity and clothing resistance, while activity level and
air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are
considered constant values for selected type of indoor environment. Clothing resistance is
adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature,
mean radiant temperature and relative humidity are values which are randomly generated
within appropriately selected ranges. Taking into account that coefficients of determination
which correspond to it are over 95%, resulting first degree polynomial relation obtained by
using multiple linear regression can be considered a satisfactory approximation of PMV
model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value
combinations for which PMV values obtained by using this model coincide with the ones
calculated by using algorithm which is provided by standard. However, results obtained by
using trained neural network with one hidden layer coincide with PMV values calculated on
the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from
the standpoint of accuracy, it is concluded that neural network provides significantly better
approximation of PMV model.",
publisher = "Faculty of Mechanical Engineering and Naval Architecture, Zagreb",
journal = "18th conference on sustainable development of energy, water and environment systems",
title = "Thermal comfort indices analysis using multiple linear regression and neural network",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7076"
}
