Iterative filtering of ground data for qualifying statistical models for solar irradiance estimation from satellite data

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.solener.2005.03.004 Byline: Jesus Polo, Luis F. Zarzalejo, Lourdes Ramirez, Bella Espinar Keywords: Solar irradiance; Meteosat satellite; Active learning; Ground database quality Abstract: A new technique of filtering solar radiation ground data is proposed for generating models for solar irradiance estimation from geostationary satellite data. The filtering processes consists of an iterative way of selecting the training data set to achieve the best model response. Although in this paper the proposed methodology has been used for solar irradiance modeling, it could be applied to any kind of empirical modeling. The iterative filtering method has proven to have fast convergence and to improve successfully the statistical model response, when applied to hourly global irradiance calculation from satellite-derived irradiances for 13 Spanish locations. Individual statistical models for hourly global irradiance were fitted using the Heliosat I method applied to Meteosat images of 13 Spanish stations for the period 1994-1996. Author Affiliation: Renewable Energy Department, CIEMAT, Avad. Complutense, 22, 28040 Madrid, Spain Article History: Received 26 January 2004; Revised 17 February 2005; Accepted 9 March 2005 Article Note: (miscellaneous) Communicated by: Associate Editor Pierre Ineichen