title: Thermal sensation prediction by soft computing methodology.
abstract: Thermal comfort in open urban areas is very factor based on environmental point of view. Therefore it is need to fulfill demands for suitable thermal comfort during urban planning and design. Thermal comfort can be modeled based on climatic parameters and other factors. The factors are variables and they are changed throughout the year and days. Therefore there is need to establish an algorithm for thermal comfort prediction according to the input variables. The prediction results could be used for planning of time of usage of urban areas. Since it is very nonlinear task, in this investigation was applied soft computing methodology in order to predict the thermal comfort. The main goal was to apply extreme leaning machine (ELM) for forecasting of physiological equivalent temperature (PET) values. Temperature, pressure, wind speed and irradiance were used as inputs. The prediction results are compared with some benchmark models. Based on the results ELM can be used effectively in forecasting of PET.
authors.affiliation: University of Priština, Faculty of Technical Sciences in Kosovska Mitrovica, Kneza Milosa 7, 38220 Kosovska Mitrovica, Serbia.
authors.forename: Srđan
authors.initials: S
authors.lastname: Jović
authors.affiliation: University of Priština, Faculty of Technical Sciences in Kosovska Mitrovica, Kneza Milosa 7, 38220 Kosovska Mitrovica, Serbia.
authors.forename: Nebojša
authors.initials: N
authors.lastname: Arsić
authors.affiliation: University of Priština, Faculty of Philosophy, Filipa Višnjića b.b., 38220 Kosovska Mitrovica, Serbia.
authors.forename: Jovana
authors.initials: J
authors.lastname: Vilimonović
authors.affiliation: University of Niš, Faculty of Mechanical Engineering, Aleksandra Medvedeva 14, 18000 Niš, Serbia. Electronic address: dalibortc@gmail.com.
authors.forename: Dalibor
authors.initials: D
authors.lastname: Petković
mesh_terms: D000465:Algorithms; D002958:City Planning; D003198:Computer Simulation; D004777:Environment; D006801:Humans; D000069550:Machine Learning; D008954:Models, Biological; D016571:Neural Networks, Computer; D013696:Temperature; D013697:Thermosensing; D014919:Wind
keywords: Extreme leaning machine; PET; Prediction; Thermal comfort; Urban areas