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

Choongwan Koo, Taehoon Hong, Jeongyoon Oh and Jun-Ki Choi

Applied Energy, 2018, vol. 215, issue C, 53 pages

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.

Keywords: Distributed generation of solar power system; Building-integrated photovoltaic blind; Finite element method; Prediction performance; Nonlinearity analysis (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261918300941
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:215:y:2018:i:c:p:41-53

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2018.01.081

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().