Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.renene.2007.04.012
DC FieldValue
dc.titleModels of diffuse solar radiation
dc.contributor.authorBoland, J.
dc.contributor.authorRidley, B.
dc.contributor.authorBrown, B.
dc.date.accessioned2016-12-13T05:35:42Z
dc.date.available2016-12-13T05:35:42Z
dc.date.issued2008-04
dc.identifier.citationBoland, J., Ridley, B., Brown, B. (2008-04). Models of diffuse solar radiation. Renewable Energy 33 (4) : 575-584. ScholarBank@NUS Repository. https://doi.org/10.1016/j.renene.2007.04.012
dc.identifier.issn09601481
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/132717
dc.description.abstractFor some locations both global and diffuse solar radiation are measured. However, for many locations, only global is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from trigonometry, we need to have diffuse on the horizontal available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse radiation on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia [Spencer JW. A comparison of methods for estimating hourly diffuse solar radiation from global solar radiation. Sol Energy 1982; 29(1): 19-32]. Boland et al. [Modelling the diffuse fraction of global solar radiation on a horizontal surface. Environmetrics 2001; 12: 103-16] developed a validated model for Australian conditions. We detail our recent advances in developing the theoretical framework for the approach reported therein, particularly the use of the logistic function instead of piecewise linear or simple nonlinear functions. Additionally, we have also constructed a method, using quadratic programming, for identifying values that are likely to be erroneous. This allows us to eliminate outliers in diffuse radiation values, the data most prone to errors in measurement. © 2007 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.renene.2007.04.012
dc.sourceScopus
dc.subjectDiffuse radiation
dc.subjectMathematical model
dc.subjectNon-parametric statistics
dc.subjectQuadratic programming
dc.subjectQuality assurance
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.renene.2007.04.012
dc.description.sourcetitleRenewable Energy
dc.description.volume33
dc.description.issue4
dc.description.page575-584
dc.identifier.isiut000252997100005
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