A Comparison of Time-Series Gap-Filling Methods to Impute Solar Radiation Data: Preprint

Alexis Denhard, Soutir Bandyopadhyay, Aron Habte, Manajit Sengupta

Research output: Contribution to conferencePaper


Complete solar resource data sets play a critical role at every stage of solar energy projects; however, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including, but not limited to, data gaps and data quality issues. To mitigate these issues, an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Motivated by this, in this study, we extensively compare the performance of eight different gap-filling methods by creating random and artificial data gaps in (i) hourly irradiance data for 1 year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) 1-minute ground measurement data sets from the Surface Radiation Budget Network (SURFRAD) and the National Renewable Energy Laboratory (NREL) stations.
Original languageAmerican English
Number of pages14
StatePublished - 2022
EventISES Solar World Congress 2021 (SWC 2021) -
Duration: 25 Oct 202129 Oct 2021


ConferenceISES Solar World Congress 2021 (SWC 2021)

Bibliographical note

See NREL/CP-5D00-84009 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-81248


  • clearness index
  • gap fill
  • global horizontal irradiance
  • time series


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