Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85526
Título: | Predictive model of clothing insulation in naturally ventilated educational buildings |
Autor(es): | Hoz-Torres, María L. de la Aguilar, Antonio J. Costa, Nélson Bruno Martins Marques da Arezes, P. Ruiz, Diego P. Martínez-Aires, Mª Dolores |
Palavras-chave: | Built environment Educational buildings Thermal environment Clothing insulation Occupant behaviour Natural ventilation |
Data: | 10-Abr-2023 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Buildings |
Citação: | de la Hoz-Torres, M.L.; Aguilar, A.J.; Costa, N.; Arezes, P.; Ruiz, D.P.; Martínez-Aires, M.D. Predictive Model of Clothing Insulation in Naturally Ventilated Educational Buildings. Buildings 2023, 13, 1002. https://doi.org/10.3390/buildings13041002 |
Resumo(s): | Providing suitable indoor thermal conditions in educational buildings is crucial to ensuring the performance and well-being of students. International standards and building codes state that thermal conditions should be considered during the indoor design process and sizing of heating, ventilation and air conditioning systems. Clothing insulation is one of the main factors influencing the occupants’ thermal perception. In this context, a field survey was conducted in higher education buildings to analyse and evaluate the clothing insulation of university students. The results showed that the mean clothing insulation values were 0.60 clo and 0.72 clo for male and female students, respectively. Significant differences were found between seasons. Correlations were found between indoor and outdoor air temperature, radiant temperature, the temperature measured at 6 a.m., and running mean temperature. Based on the collected data, a predictive clothing insulation model, based on an artificial neural network (ANN) algorithm, was developed using indoor and outdoor air temperature, radiant temperature, the temperature measured at 6 a.m. and running mean temperature, gender, and season as input parameters. The ANN model showed a performance of R2 = 0.60 and r = 0.80. Fifty percent of the predicted values differed by less than 0.1 clo from the actual value, whereas this percentage only amounted to 32% if the model defined in the ASHRAE-55 Standard was applied. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85526 |
DOI: | 10.3390/buildings13041002 |
e-ISSN: | 2075-5309 |
Versão da editora: | https://www.mdpi.com/2075-5309/13/4/1002 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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buildings-13-01002.pdf | 4,14 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons