Modeling hourly solar diffuse fraction on a horizontal surface based on sky conditions clustering

Kaili Yin, Xiaojing Zhang, Jingchao Xie, Ziyang Hao, Guofeng Xiao and Jiaping Liu

Energy, 2023, vol. 272, issue C

Abstract: The commonly used diffuse fraction models are generally expressed as a single-value function of clearness index (Kt). Whereas in fact, the same Kt occurring at different times of the day may correspond to different diffuse fraction (Kf). Using a 10-year dataset from 2005 to 2014, this study intended to model hourly solar diffuse fraction based on sky conditions. Five cities in China, including Golmud, Kashgar, Sanya, Wuhan, and Wenjiang, were selected as representatives of five solar climatic zones. The measured radiation data on a horizontal surface were used for k-means clustering of the sky conditions with two factors of Kf and Kt. The results show that the sky conditions could be divided into two groups according to time dimension: the morning (6:00–12:00) and the afternoon (13:00–18:00) segments. Subsequently, a morning-afternoon segmented Kf-Kt model for each zone was constructed based on quartic polynomial regression, with the smallest error rate at a range of 10.4%–28.5%. Comparison with the existing models further validates that the respective models for the morning and afternoon periods can assist in improving the modeling of the hourly solar diffuse fraction on a horizontal surface in variable solar climatic zones.

Keywords: Clearness index; Diffuse fraction; Solar radiation; K-means; Polynomial regression (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223004024
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:energy:v:272:y:2023:i:c:s0360544223004024

DOI: 10.1016/j.energy.2023.127008

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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