Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment

dc.contributor.author Ozbey, Mehmet Furkan
dc.contributor.author Lotfi, Bahram
dc.contributor.author Turhan, Cihan
dc.contributor.other Energy Systems Engineering
dc.contributor.other Mechanical Engineering
dc.date.accessioned 2025-03-05T20:47:04Z
dc.date.available 2025-03-05T20:47:04Z
dc.date.issued 2025
dc.description.abstract Thermal comfort describes an occupant's state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, T & uuml;rkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R-2 value of 0.94 compared to the globe thermometer measurement method. The model's advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT. en_US
dc.identifier.doi 10.1080/17512549.2025.2457650
dc.identifier.issn 1751-2549
dc.identifier.issn 1756-2201
dc.identifier.scopus 2-s2.0-85216452348
dc.identifier.uri https://doi.org/10.1080/17512549.2025.2457650
dc.identifier.uri https://hdl.handle.net/20.500.14411/10473
dc.language.iso en en_US
dc.publisher Taylor & Francis Ltd en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Thermal Comfort en_US
dc.subject Artificial Neural Network en_US
dc.subject Mean Radiant Temperature en_US
dc.subject Indoor Environment en_US
dc.subject Office Buildings en_US
dc.title Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Özbey, Mehmet Furkan
gdc.author.institutional Turhan, Cihan
gdc.author.scopusid 57219871456
gdc.author.scopusid 55346613600
gdc.author.scopusid 56011415300
gdc.author.wosid Turhan, Cihan/Abd-1880-2021
gdc.author.wosid Özbey, Mehmet Furkan/Glu-8252-2022
gdc.coar.access metadata only access
gdc.coar.type text::journal::journal article
gdc.description.department Atılım University en_US
gdc.description.departmenttemp [Ozbey, Mehmet Furkan] Atilim Univ, Grad Sch Nat & Appl Sci, Mech Engn Dept, TR-06830 Ankara, Turkiye; [Lotfi, Bahram] TOBB Univ Econ & Technol, Mech Engn Dept, Ankara, Turkiye; [Turhan, Cihan] Atilim Univ, Energy Syst Engn Dept, Ankara, Turkiye en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q2
gdc.description.woscitationindex Emerging Sources Citation Index
gdc.description.wosquality N/A
gdc.identifier.wos WOS:001409585500001
gdc.scopus.citedcount 0
gdc.wos.citedcount 0
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