An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief

Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness b...

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Main Authors: Alireza Entezari, Fatemeh Mayvaneh, Khosro Rezaie, Fatemeh Rahimi
Format: Article
Language:fas
Published: Kharazmi University 2018-06-01
Series:تحقیقات کاربردی علوم جغرافیایی
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Online Access:http://jgs.khu.ac.ir/article-1-2727-en.pdf
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author Alireza Entezari
Fatemeh Mayvaneh
Khosro Rezaie
Fatemeh Rahimi
author_facet Alireza Entezari
Fatemeh Mayvaneh
Khosro Rezaie
Fatemeh Rahimi
author_sort Alireza Entezari
collection DOAJ
description Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray Man software environment. Then UTCI and PMV index values were calculated using Bioklima software. The results showed that the most severe cold temperature stress on PMV index is in the winter and late autumn and UTCI index in January and February are the coldest stress. The power of neural networks, prediction of future performance network (generalized orientation) it simply is not possible and the new model presented in this paper have been restricted Boltzmann machine-based neural networks or neural networks is used deep belief. Using this structure, metrics Mean Squared Error (MSE) and mean absolute percentage error (MAPE) benchmark ate for seven indexes derived from data gathered by three factors related to the occurrence of weather conditions and other indicators of thermal comfort of human the system was evaluated. Assessment by dividing the data into training and testing parts, and the ratios have been of two-thirds, fifty percent and one-third And two benchmark MSE and MAPE were calculated. The proposed system performance in forecasting the human thermal comfort is desirable.
format Article
id doaj-art-cbe46776836944bd9f38726519cf26d2
institution Kabale University
issn 2228-7736
2588-5138
language fas
publishDate 2018-06-01
publisher Kharazmi University
record_format Article
series تحقیقات کاربردی علوم جغرافیایی
spelling doaj-art-cbe46776836944bd9f38726519cf26d22025-01-31T17:25:06ZfasKharazmi Universityتحقیقات کاربردی علوم جغرافیایی2228-77362588-51382018-06-0118512340An adaptive estimation method to predict thermal comfort indices man using car classification neural deep beliefAlireza Entezari0Fatemeh Mayvaneh1Khosro Rezaie2Fatemeh Rahimi3 Assistant Professor of climatology, Hakim Sabzevari University, Sabzevar, Iran. Ph.D. Student of Climatology, Hakim Sabzevari University, Sabzevar, Iran. Assistant Professor of Medical Engineering, Hakim Sabzevari University, Sabzevar, Iran. Graduate student of Climatology, University of Sabzevar Landscape, Sabzevar, Iran. Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray Man software environment. Then UTCI and PMV index values were calculated using Bioklima software. The results showed that the most severe cold temperature stress on PMV index is in the winter and late autumn and UTCI index in January and February are the coldest stress. The power of neural networks, prediction of future performance network (generalized orientation) it simply is not possible and the new model presented in this paper have been restricted Boltzmann machine-based neural networks or neural networks is used deep belief. Using this structure, metrics Mean Squared Error (MSE) and mean absolute percentage error (MAPE) benchmark ate for seven indexes derived from data gathered by three factors related to the occurrence of weather conditions and other indicators of thermal comfort of human the system was evaluated. Assessment by dividing the data into training and testing parts, and the ratios have been of two-thirds, fifty percent and one-third And two benchmark MSE and MAPE were calculated. The proposed system performance in forecasting the human thermal comfort is desirable.http://jgs.khu.ac.ir/article-1-2727-en.pdfthermal comfortneural networkweather conditionshuman health.
spellingShingle Alireza Entezari
Fatemeh Mayvaneh
Khosro Rezaie
Fatemeh Rahimi
An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
تحقیقات کاربردی علوم جغرافیایی
thermal comfort
neural network
weather conditions
human health.
title An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
title_full An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
title_fullStr An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
title_full_unstemmed An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
title_short An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
title_sort adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
topic thermal comfort
neural network
weather conditions
human health.
url http://jgs.khu.ac.ir/article-1-2727-en.pdf
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