Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77531
Title: Improving the prediction performance of the finite element model for estimating the technical performance of the distributed generation of solar power system in a building façade
Authors: Koo, C 
Hong, T
Oh, J
Choi, JK
Keywords: Building-integrated photovoltaic blind
Distributed generation of solar power system
Finite element method
Nonlinearity analysis
Prediction performance
Issue Date: 2018
Publisher: Pergamon Press
Source: Applied energy, 2018, v. 215, p. 41-53 How to cite?
Journal: Applied energy 
Abstract: As interest in the distributed generation of solar power system in a building façade continues to increase, its technical performance (i.e. the amount of electricity generation) should be carefully investigated before its implementation. In this regard, this study aimed to develop the nine-node-based finite element model for estimating the technical performance of the distributed generation of solar power system in a building façade (FEM9- node), focusing on the improvement of the prediction performance. The developed model (FEM9- node) was proven to be superior to the four-node-based model (FEM4- node), which was developed in the previous study, in terms of both prediction accuracy and standard deviation. In other words, the prediction accuracy (3.55%) and standard deviation (2.93%) of the developed model (FEM9- node) was determined to be superior to those of the previous model (FEM4- node) (i.e. 4.54% and 4.39%, respectively). The practical application was carried out to enable a decision maker (e.g. construction manager, facility manager) to understand how the developed model works in a clear way. It is expected that the developed model (FEM9- node) can be used in the early design phase in an easy way within a short time. In addition, it could be extended to any other countries in a global environment.
URI: http://hdl.handle.net/10397/77531
ISSN: 0306-2619
EISSN: 1872-9118
DOI: 10.1016/j.apenergy.2018.01.081
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