A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies

A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies

Sustaining outdoor life in cities is decreasing because of the recent rapid urbanisation without considering climate-responsive urban design concepts. Such inadvertent climatic modifications at the indoor level have imposed considerable demand on the urban energy resources. It is important to provid...

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Bibliographic Details
Published in:Energy
Main Author: Kariminia S.; Shamshirband S.; Hashim R.; Saberi A.; Petković D.; Roy C.; Motamedi S.
Format: Article
Language:English
Published: Elsevier Ltd 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977648948&doi=10.1016%2fj.energy.2016.02.021&partnerID=40&md5=b4acc93c81bd61144a06f95b81bc0b5c
id 2-s2.0-84977648948
spelling 2-s2.0-84977648948
Kariminia S.; Shamshirband S.; Hashim R.; Saberi A.; Petković D.; Roy C.; Motamedi S.
A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
2016
Energy
101

10.1016/j.energy.2016.02.021
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977648948&doi=10.1016%2fj.energy.2016.02.021&partnerID=40&md5=b4acc93c81bd61144a06f95b81bc0b5c
Sustaining outdoor life in cities is decreasing because of the recent rapid urbanisation without considering climate-responsive urban design concepts. Such inadvertent climatic modifications at the indoor level have imposed considerable demand on the urban energy resources. It is important to provide comfortable ambient climate at open urban squares. Researchers need to predict the comfortable conditions at such outdoor squares. The main objective of this study is predict the visitors' outdoor comfort indices by using a developed computational model termed as SVM-WAVELET (Support Vector Machines combined with Discrete Wavelet Transform algorithm). For data collection, the field study was conducted in downtown Isfahan, Iran (51°41′ E, 32°37′ N) with hot and arid summers. Based on different environmental elements, four separate locations were monitored across two public squares. Meteorological data were measured simultaneously by surveying the visitors' thermal sensations. According to the subjects' thermal feeling and their characteristics, their level of comfort was estimated. Further, the adapted computational model was used to estimate the visitors’ thermal sensations in terms of thermal comfort indices. The SVM-WAVELET results indicate that R2 value for input parameters, including Thermal Sensation, PMW (The predicted mean vote), PET (physiologically equivalent temperature), SET (standard effective temperature) and Tmrt were estimated at 0.482, 0.943, 0.988, 0.969 and 0.840, respectively. © 2016 Elsevier Ltd
Elsevier Ltd
3605442
English
Article
author Kariminia S.; Shamshirband S.; Hashim R.; Saberi A.; Petković D.; Roy C.; Motamedi S.
spellingShingle Kariminia S.; Shamshirband S.; Hashim R.; Saberi A.; Petković D.; Roy C.; Motamedi S.
A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
author_facet Kariminia S.; Shamshirband S.; Hashim R.; Saberi A.; Petković D.; Roy C.; Motamedi S.
author_sort Kariminia S.; Shamshirband S.; Hashim R.; Saberi A.; Petković D.; Roy C.; Motamedi S.
title A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
title_short A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
title_full A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
title_fullStr A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
title_full_unstemmed A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
title_sort A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies
publishDate 2016
container_title Energy
container_volume 101
container_issue
doi_str_mv 10.1016/j.energy.2016.02.021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977648948&doi=10.1016%2fj.energy.2016.02.021&partnerID=40&md5=b4acc93c81bd61144a06f95b81bc0b5c
description Sustaining outdoor life in cities is decreasing because of the recent rapid urbanisation without considering climate-responsive urban design concepts. Such inadvertent climatic modifications at the indoor level have imposed considerable demand on the urban energy resources. It is important to provide comfortable ambient climate at open urban squares. Researchers need to predict the comfortable conditions at such outdoor squares. The main objective of this study is predict the visitors' outdoor comfort indices by using a developed computational model termed as SVM-WAVELET (Support Vector Machines combined with Discrete Wavelet Transform algorithm). For data collection, the field study was conducted in downtown Isfahan, Iran (51°41′ E, 32°37′ N) with hot and arid summers. Based on different environmental elements, four separate locations were monitored across two public squares. Meteorological data were measured simultaneously by surveying the visitors' thermal sensations. According to the subjects' thermal feeling and their characteristics, their level of comfort was estimated. Further, the adapted computational model was used to estimate the visitors’ thermal sensations in terms of thermal comfort indices. The SVM-WAVELET results indicate that R2 value for input parameters, including Thermal Sensation, PMW (The predicted mean vote), PET (physiologically equivalent temperature), SET (standard effective temperature) and Tmrt were estimated at 0.482, 0.943, 0.988, 0.969 and 0.840, respectively. © 2016 Elsevier Ltd
publisher Elsevier Ltd
issn 3605442
language English
format Article
accesstype
record_format scopus
collection Scopus
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