THERMAL SCIENCE

International Scientific Journal

FORECASTING OF OUTDOOR THERMAL COMFORT INDEX IN URBAN OPEN SPACES THE NIS FORTRESS CASE STUDY

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.
KEYWORDS
PAPER SUBMITTED: 2016-04-19
PAPER REVISED: 2016-08-08
PAPER ACCEPTED: 2016-09-09
PUBLISHED ONLINE: 2016-12-25
DOI REFERENCE: https://doi.org/10.2298/TSCI16S5531B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2016, VOLUME 20, ISSUE Supplement 5, PAGES [S1531 - S1539]
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, gis.ni.rs/
  10. ***, PUC Institute for Urban Planning Nis, www.zurbnis.rs/
  11. ***, Tourist Organization Nis, 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

© 2021 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence