Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6686
Title: Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators
Authors: Turhan, Cihan
Kazanasmaz, Zehra Tuğçe
Gökçen Akkurt, Gülden
Turhan, Cihan
Kazanasmaz, Zehra Tuğçe
Gökçen Akkurt, Gülden
Izmir Institute of Technology. Mechanical Engineering
Izmir Institute of Technology. Mechanical Engineering
Izmir Institute of Technology. Energy Systems Engineering
Izmir Institute of Technology. Architecture
Keywords: ANFIS
Fuzzy logic
Heat load
Residential buildings
Soft computing methods
Issue Date: Aug-2017
Publisher: Yıldız Teknik Üniversitesi
Source: Turhan, C., Kazanasmaz, T., and Gökçen Akkurt, G. (2017). Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators. Journal of Thermal Engineering, 3(4), 1358-1374. doi:10.18186/journal-of-thermal-engineering.330180
Abstract: This study estimates the heat load of buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) and compares their prediction indices. Obtaining knowledge about what the heat load of buildings would be in architectural design stage is necessary to forecast the building performance and take precautions against any possible failure. The best accuracy and prediction power of novel soft computing techniques would assist the practical way of this process. For this purpose, four inputs, namely, wall overall heat transfer coefficient, building area/ volume ratio, total external surface area and total window area/total external surface area ratio were employed in each model of this study. The predicted heat load is evaluated comparatively using simulation outputs. The ANN model estimated the heat load of the case apartments with a rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these values were compared, it was found that the ANFIS model has become the best learning technique among the others and can be applicable in building energy performance studies.
URI: http://doi.org/10.18186/journal-of-thermal-engineering.330180
http://hdl.handle.net/11147/6686
ISSN: 2148-7847
2148-7847
Appears in Collections:Architecture / Mimarlık
Energy Systems Engineering / Enerji Sistemleri Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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