FORECASTING OF OUTDOOR THERMAL COMFORT INDEX IN URBAN OPEN SPACES The Nis Fortress Case Study

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Ivana S. BOGDANOVIĆ PROTIĆ Ana V. VUKADINOVIĆ Jasmina M. RADOSAVLJEVIĆ Meysam ALIZAMIR Mihajlo P. MITKOVIĆ

Abstract

Outdoor thermal environment is affected by variables like air temperature, wind velocity, humidity, temperature of the radiant surfaces, and solar  radiation, which can be expressed by a single number – the thermal index. Since these variables are subject to annual and diurnal variations, prediction of thermal comfort is of special importance for people to plan their outdoor activities. The purpose  of this research was to develop and apply the extreme learning machine for forecasting physiological equivalent temperature values. The results of the extreme learning machine model were compared with genetic programming and artificial neural network. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. According to obtained results, it can be concluded that extreme learning machine can be utilized effectively in short term forecasting of physiological equivalent temperature.

Article Details

How to Cite
BOGDANOVIĆ PROTIĆ, Ivana S. et al. FORECASTING OF OUTDOOR THERMAL COMFORT INDEX IN URBAN OPEN SPACES. Thermal Science, [S.l.], v. 20, p. S1531-S1539, feb. 2017. ISSN 2334-7163. Available at: <http://thermal-science.tech/journal/index.php/thsci/article/view/1669>. Date accessed: 18 oct. 2017. doi: https://doi.org/10.2298/TSCI16S5531B.
Section
Articles
Received 2017-02-07
Accepted 2017-02-07
Published 2017-02-07

References

[1] Ghasemi, Z., et al., Promotion of Urban Environment by Consideration of Human Thermal & Wind Comfort: A Literature Review, Procedia Soc. Behav. Sci., 201 (2015), Aug., pp. 397-408
[2] Cohen, P., et al., Daily and Seasonal Climatic Conditions of Green Urban Open Spaces in the Mediterranean Climate and Their Impact on Human Comfort, Build Environ., 51 (2012), May, pp. 285-295
[3] Taleghani, M., et al., A Review into Thermal Comfort in Buildings, Renew. Sustainable Energy Rev., 26 (2013), Oct., pp. 201-215
[4] Charalampopoulos, I., et al., Analysis of Thermal Bioclimate in Various Urban Configurations in Athens, Greece, Urban Ecosyst., 16 (2013), 2, pp. 217-233
[5] Daneshvar, М., et al., Assessment of Bioclimatic Comfort Conditions Based on Physiologically Equivalent Temperature (PET) using the RayMan Model in Iran, Cent. Eur. J. Geosci., 5 (2013), 1, pp. 53-60
[6] Salata, T., et al., Outdoor Thermal Comfort in the Mediterranean Area. A Transversal Study in Rome, Italy, Build Environ., 96 (2016), Feb., pp. 46-61
[7] Algeciras, J. A., Matzarakis, A., Quantification of Thermal Bioclimate for the Management of Urban Design in Mediterranean Climate of Barcelona, Spain, Int. J. Biometeorol., 60 (2015), 8, pp. 1-10
[8] Matzarakis, А., et al., Applications of a Universal Thermal Index: Physiological Equivalent Temperature, Int. J. Biometeorol., 43 (1999), 2, pp. 76-84
[9] ***, Nis-Ortophoto Wider City Area, http://gis.ni.rs/
[10] ***, PUC Institute for Urban Planning Nis, http://www.zurbnis.rs/
[11] ***, Tourist Organization Nis, http://www.visitnis.com
[12] Matzarakis, A., et al., Modelling Radiation Fluxes in Simple and Complex Environments – Application of the RayMan Model, Int. J. Biometeorol., 51 (2007), 4, pp. 323-334
[13] Huang, G. B., et al., Extreme Learning Machine: Theory and Applications, Neurocomputing, 70 ( 2006), 1, pp. 489-501
[14] Huang, G. B., et al., Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes, IEEE Trans. Neural Netw., 17 (2006), 4, pp. 879-892

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