Compensation of temporal averaging bias in solar irradiance data. Issue 10 (8th June 2017)

• Record Type: Journal Article
• Authors: Gibson, Keith; Cole, Ian R.; Goss, Brian; Betts, Thomas R.; Gottschalg, Ralph
• Is Part Of: IET renewable power generation. Volume 11:Issue 10(2017)
• Page Start: 1288
• Page End: 1294
• Publication Date: 2017-06-08
• Subjects: solar power -- statistical distributions -- mean square error methods; temporal averaging bias -- solar irradiance data -- solar energy system performance prediction -- energy yield estimation -- irradiance uncertainty -- component deconstruction -- plane translation mechanisms -- industry standard -- hourly averaged data -- clearness index redistribution method -- statistical redistribution -- CREST -- Loughborough University -- root mean square error -- beam yield -- global yield; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2016.0903
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Solar irradiance data is used for the prediction of solar energy system performance but is presently a significant source of uncertainty in energy yield estimation. This also directly affects the expected revenue, so the irradiance uncertainty contributes to project risk and therefore the cost of finance. In this study, the combined impact of temporal averaging, component deconstruction and plane translation mechanisms on uncertainty is analysed. A new method to redistribute (industry standard) hourly averaged data is proposed. This clearness index redistribution method is based on the statistical redistribution of clearness index values and largely corrects the bias error introduced by temporal averaging. Parameters for the redistribution model were derived using irradiance data measured at high temporal resolution by CREST, Loughborough University, over a 5‐year period. The root mean square error of example net annual (2014) diffuse, beam and global yield of hourly averaged data were reduced from ∼15 to 1, 14 to 3 and 4 to 1%, respectively.