Characterization of insolation data for use in photovoltaic system analysis models

S. Rahman, M.A. Khallat and Z.M. Salameh

Energy, 1988, vol. 13, issue 1, 63-72

Abstract: A statistical technique to characterize insolation data for use in photovoltaic (PV) systems is presented. We start by examining the frequency distribution of long-term insolation data. The histogram is generated for observed insolation for a particular hour over a month for a number of years. It is fitted to three distributions (Weibull, β and log normal). Four goodness-of-fit criteria are employed in checking the best fit. These are Chi-square, Kolmogorov-Smirnov, Cramer-Von Mises-Smirnov, and log-likelihood. SOLMET data from Sterling, Va, Raleigh-Durham, N.C. and Miami, Fla are analyzed. It is found that the β distribution fits the long-term hourly global horizontal insolation data best for these three southeastern U.S. locations.

Date: 1988
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0360544288900795
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:13:y:1988:i:1:p:63-72

DOI: 10.1016/0360-5442(88)90079-5

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 (repec@elsevier.com).