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      Compensation of temporal averaging bias in solar irradiance data

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          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.

          Most cited references10

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          The interrelationship and characteristic distribution of direct, diffuse and total solar radiation

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            Correlation equation for hourly diffuse radiation on a horizontal surface

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              Modelling of diffuse solar fraction with multiple predictors

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                Author and article information

                Contributors
                Journal
                IET-RPG
                IET Renewable Power Generation
                IET Renew. Power Gener.
                The Institution of Engineering and Technology
                1752-1416
                1752-1424
                2 May 2017
                8 June 2017
                16 August 2017
                : 11
                : 10
                : 1288-1294
                Affiliations
                Centre for Renewable Energy Systems Technology (CREST), School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University , LE11 3TU, UK
                Author information
                https://orcid.org/0000-0001-8131-0964
                Article
                IET-RPG.2016.0903 RPG.SI.2016.0903.R1
                10.1049/iet-rpg.2016.0903
                a71df79c-b7d1-4dc4-afa9-242d24a04fd3

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                History
                : 9 November 2016
                : 30 March 2017
                : 21 April 2017
                Page count
                Pages: 0
                Funding
                Funded by: Engineering and Physical Sciences Research Council
                Award ID: EP/K02227X/1
                Categories
                Special Issue: Performance Assessment and Condition Monitoring of Photovoltaic Systems for Improved Energy Yield

                Computer science,Engineering,Artificial intelligence,Electrical engineering,Mechanical engineering,Renewable energy
                irradiance uncertainty,root mean square error,solar energy system performance prediction,global yield,statistical distributions,Loughborough University,energy yield estimation,CREST,solar power,component deconstruction,statistical redistribution,plane translation mechanisms,clearness index redistribution method,industry standard,hourly averaged data,mean square error methods,beam yield,temporal averaging bias,solar irradiance data

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