Potential of support vector regression for solar radiation prediction in Nigeria

Lanre Olatomiwa, Saad Mekhilef (), Shahaboddin Shamshirband () and Dalibor Petkovic

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 77, issue 2, 1055-1068

Abstract: In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (T max ), monthly mean minimum temperature (T min ) and monthly mean sunshine duration ( $$ \bar{n} $$ n ¯ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: SVR; Solar radiation; Sunshine hour; Soft computing methodologies; Nigeria (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11069-015-1641-x (text/html)
Access to full text is restricted to subscribers.

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:spr:nathaz:v:77:y:2015:i:2:p:1055-1068

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-015-1641-x

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().