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izvor podataka: crosbi

Application of Neural Networks in Predicting Airtightness of Residential Units (CROSBI ID 208141)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Krstić, Hrvoje ; Koški, Željko ; Ištoka Otković, Irena ; Španić, Martina Application of Neural Networks in Predicting Airtightness of Residential Units // Energy and buildings, 84 (2014), 160-168. doi: 10.1016/j.enbuild.2014.08.007

Podaci o odgovornosti

Krstić, Hrvoje ; Koški, Željko ; Ištoka Otković, Irena ; Španić, Martina

engleski

Application of Neural Networks in Predicting Airtightness of Residential Units

The paper describes the need to investigate airtightness of existing residential units in order to achieve adequate energy efficiency. There is a brief explanation of the blower door method used in field testing of airtightness in residential units. The mentioned method was used to create a database of airtightness test results obtained in situ at 58 residential units in the local area. The analysis of the results of these measurements indicates that there are four input parameters, which had been previously investigated and evaluated, that had a dominant effect on the test results. The examining of the possibility of successfully predicting airtightness was conducted by applying neural networks. The response results indicate that there is potential for successful prediction of airtightness, which was confirmed by independent validation through 20 additional measurements. In order to be able to generalize the results, further research needs to be conducted, using a bigger sample of measured data, and additional configurations of neural networks need to be examined.

airtightness; residential units; neural networks

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Podaci o izdanju

84

2014.

160-168

objavljeno

0378-7788

10.1016/j.enbuild.2014.08.007

Povezanost rada

Arhitektura i urbanizam, Građevinarstvo

Poveznice
Indeksiranost