Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey

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dc.contributor.authorOzbalta, Turkan Goksal
dc.contributor.authorSezer, Alper
dc.contributor.authorYildiz, Yusuf
dc.date.accessioned2019-10-27T21:40:46Z
dc.date.available2019-10-27T21:40:46Z
dc.date.issued2012
dc.departmentEge Üniversitesien_US
dc.description.abstractIn this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot-humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R-2) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.en_US
dc.identifier.doi10.1177/1420326X11422163
dc.identifier.endpage781en_US
dc.identifier.issn1420-326X
dc.identifier.issn1423-0070
dc.identifier.issn1420-326Xen_US
dc.identifier.issn1423-0070en_US
dc.identifier.issue6en_US
dc.identifier.startpage772en_US
dc.identifier.urihttps://doi.org/10.1177/1420326X11422163
dc.identifier.urihttps://hdl.handle.net/11454/46350
dc.identifier.volume21en_US
dc.identifier.wosWOS:000311796000004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofIndoor and Built Environmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectMultiple regressionen_US
dc.subjectModellingen_US
dc.subjectIndoor temperature and relative humidityen_US
dc.titleModels for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkeyen_US
dc.typeArticleen_US

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