PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS

Year 2017, , 1358 - 1374, 21.07.2017

Gülden Gökçen Akkurt

https://doi.org/10.18186/journal-of-thermal-engineering.330179

Cited By: 4

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.

Keywords

Heat Load, Residential Buildings, ANN, Fuzzy Logic, ANFIS

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