Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2006
Title: Artificial neural networks applications in building energy predictions and a case study for tropical climates
Authors: Yalçıntaş, Melek
Akkurt, Sedat
Akkurt, Sedat
Keywords: Artificial neural network
Building
Climate
Energy use
Energy utilization
Issue Date: Aug-2005
Publisher: John Wiley and Sons Inc.
Source: Yalçıntaş, M., and Akkurt, S. (2005). Artificial neural networks applications in building energy predictions and a case study for tropical climates. International Journal of Energy Research, 29(10), 891-901. doi:10.1002/er.1105
Abstract: This study presents artificial neural network (ANN) methods in building energy use predictions. Applications of the ANN methods in energy audits and energy savings predictions due to building retrofits are emphasized. A generalized ANN model that can be applied to any building type with minor modifications would be a very useful tool for building engineers. ANN methods offer faster learning time, simplicity in analysis and adaptability to seasonal climate variations and changes in the building's energy use when compared to other statistical and simulation models. The model herein is presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations. It was successfully created based on both climatic and chiller data. The average absolute training error for the model was 9.7% while the testing error was 10.0%. This indicates that the model can successfully predict the particular chiller energy consumption in a tropical climate.
URI: http://doi.org/10.1002/er.1105
http://hdl.handle.net/11147/2006
ISSN: 0363-907X
0363-907X
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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