Abstract:
One of the approaches toward determining the degree of microclimate comfort is measuring
its individual components: temperature, air velocity, relative humidity, and air quality. A
significant disadvantage of this approach is the neglect of the mutual influence of microclimate
parameters on each other. To improve the accuracy of determining microclimate comfort, it
is necessary to use a complex predicted mean vote (PMV) indicator. The PMV equation is
complex and computationally consuming; simplified solutions can be obtained using Fanger’s
diagrams, Excel calculation programs, and specialized computer applications. With the
development of technology, intelligent microclimate systems are gaining popularity. In this
article, for selecting one of the most effective intelligent technologies, models have been
developed for assessing the PMV indicator using the frameworks of fuzzy logic and neural
networks. The data obtained using the calculation program of the researchers of the Federal
State Unitary Enterprise Research Institute (Russia) were used as input parameters for the
models’ development. The program’s performance was validated against the PMV parameter
values in the ISO 7730:2005 standard, and a good agreement was found. The PMV index
values produced by the considered models were compared to the values calculated using the
program, to determine the operability and efficiency of the developed models. Our analysis
suggests that neural networks perform better on the assessment of thermal comfort, compared
with fuzzy systems.
