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dc.contributor.authorTurhan, Cihan
dc.contributor.authorKazanaşmaz, Tuğçe
dc.contributor.authorErlalelitepe Uygun, İlknur
dc.contributor.authorEkmen, Kenan Evren
dc.contributor.authorGökçen Akkurt, Gülden
dc.date.accessioned2017-06-14T06:30:34Z
dc.date.available2017-06-14T06:30:34Z
dc.date.issued2014-12
dc.identifier.citationTurhan, C., Kazanasmaz, T., Erlalelitepe Uygun, İ., Ekmen, K.E., and Gökçen Akkurt, G. (2014). Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation. Energy and Buildings, 85, 115-125. doi:10.1016/j.enbuild.2014.09.026en_US
dc.identifier.issn0378-7788
dc.identifier.urihttps://doi.org/10.1016/j.enbuild.2014.09.026
dc.identifier.urihttp://hdl.handle.net/11147/5757
dc.description.abstractThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK-109M450)en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relationinfo:eu-repo/grantAgreement/TUBITAK/MAG/109M450en_US
dc.relation.isversionof10.1016/j.enbuild.2014.09.026en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectExisting buildingsen_US
dc.subjectHeat loaden_US
dc.subjectPredictionen_US
dc.subjectResidential buildingsen_US
dc.subjectSimulation softwareen_US
dc.titleComparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimationen_US
dc.typearticleen_US
dc.contributor.authorIDTR103337en_US
dc.contributor.authorIDTR28229en_US
dc.contributor.authorIDTR114831en_US
dc.contributor.authorIDTR130569en_US
dc.contributor.iztechauthorTurhan, Cihan
dc.contributor.iztechauthorKazanaşmaz, Tuğçe
dc.contributor.iztechauthorErlalelitepe Uygun, İlknur
dc.contributor.iztechauthorEkmen, Kenan Evren
dc.contributor.iztechauthorGökçen Akkurt, Gülden
dc.relation.journalEnergy and Buildingsen_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Makina Mühendisliği Bölümüen_US
dc.identifier.volume85en_US
dc.identifier.startpage115en_US
dc.identifier.endpage125en_US
dc.identifier.wosWOS:000348880900012
dc.identifier.scopusSCOPUS:2-s2.0-84908321499
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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