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Development and Analysis of Models for Assessing Predicted Mean Vote Using Intelligent Technologies
International Journal of Fuzzy Logic and Intelligent Systems 2020;20(4):324-335
Published online December 25, 2020
© 2020 Korean Institute of Intelligent Systems.

L. Zh. Sansyzbay1, B. B. Orazbayev1 and W. Wójcik 2

1Faculty of Information Technology, Department of System Analysis and Control, L. N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan
2Lublin University of Technology, Lublin, Poland
Correspondence to: L. Zh. Sansyzbay (sansyzbaylazzat@gmail.com)
Received November 27, 2020; Revised December 14, 2020; Accepted December 15, 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords : Microclimate parameters, ISO 7730:2005 standard, PMV thermal comfort index, Fanger’s thermal comfort model, Fuzzy logic, Neural networks, MATLAB software package