Evaluation of Time-Series Gap-Filling Methods for Solar Irradiance Applications

Alexis Denhard, Soutir Bandyopadhyay, Aron Habte, Manajit Sengupta

Research output: NRELTechnical Report


A complete solar resource data set is essential for any stage of a solar energy project - from feasibility studies to daily operations. But measured or modeled solar resource data are prone to data gaps and data quality issues. To mitigate these issues, a data imputation process should be implemented to obtain a complete and reliable temporal and spatial data series. This study focused on imputing temporal scales by applying random and artificial data gaps and then implementing eight imputation methods, including the Kalman filtering and smoothing and stine interpolations. These methods were implemented on 1-minute to half hourly irradiance data for 1 year using a few locations from the National Solar Radiation Database (NSRDB) and ground measurement data set. The results demonstrated that some of the simpler methods, such as the stine and linear interpolation methods, were the relatively best models based on the statistical metrics for imputing NSRDB and ground measurement data, respectively.
Original languageAmerican English
Number of pages41
StatePublished - 2021

NREL Publication Number

  • NREL/TP-5D00-79987


  • data
  • gap filling
  • ground measurement
  • National Solar Radiation Database
  • solar resource
  • spatial scale
  • statellite data
  • temporal scale


Dive into the research topics of 'Evaluation of Time-Series Gap-Filling Methods for Solar Irradiance Applications'. Together they form a unique fingerprint.

Cite this