Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5757
Title: Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation
Authors: Turhan, Cihan
Kazanasmaz, Zehra Tuğçe
Erlalelitepe Uygun, İlknur
Ekmen, Kenan Evren
Gökçen Akkurt, Gülden
Turhan, Cihan
Kazanasmaz, Zehra Tuğçe
Erlalelitepe Uygun, İlknur
Ekmen, Kenan Evren
Gökçen Akkurt, Gülden
Izmir Institute of Technology. Mechanical Engineering
Keywords: Artificial neural networks
Existing buildings
Heat load
Prediction
Residential buildings
Simulation software
Issue Date: Dec-2014
Publisher: Elsevier Ltd.
Source: Turhan, 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.2014.09.026
Abstract: The 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.
URI: https://doi.org/10.1016/j.enbuild.2014.09.026
http://hdl.handle.net/11147/5757
ISSN: 0378-7788
0378-7788
Appears in Collections:Architecture / Mimarlık
Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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