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

Alexis Denhard; Soutir Bandyopadhyay; Aron Habte; Manajit Sengupta

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

Alexis Denhard, Soutir Bandyopadhyay, Aron Habte, Manajit Sengupta

Power Systems Engineering

Colorado School of Mines

Research output: Contribution to conference › Paper

Abstract

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 language	American English
Number of pages	14
State	Published - 2022
Event	ISES Solar World Congress 2021 (SWC 2021) - Duration: 25 Oct 2021 → 29 Oct 2021

Conference

Conference	ISES Solar World Congress 2021 (SWC 2021)
Period	25/10/21 → 29/10/21

Bibliographical note

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

NREL Publication Number

NREL/CP-5D00-81248

Keywords

clearness index
gap fill
global horizontal irradiance
time series

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://www.nrel.gov/docs/fy22osti/81248.pdf

Cite this

@conference{c489d199abfa488fa6f754280a17485a,

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

abstract = "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.",

keywords = "clearness index, gap fill, global horizontal irradiance, time series",

author = "Alexis Denhard and Soutir Bandyopadhyay and Aron Habte and Manajit Sengupta",

note = "See NREL/CP-5D00-84009 for paper as published in proceedings; ISES Solar World Congress 2021 (SWC 2021) ; Conference date: 25-10-2021 Through 29-10-2021",

year = "2022",

language = "American English",

}

TY - CONF

T1 - A Comparison of Time-Series Gap-Filling Methods to Impute Solar Radiation Data: Preprint

AU - Denhard, Alexis

AU - Bandyopadhyay, Soutir

AU - Habte, Aron

AU - Sengupta, Manajit

N1 - See NREL/CP-5D00-84009 for paper as published in proceedings

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - clearness index

KW - gap fill

KW - global horizontal irradiance

KW - time series

M3 - Paper

T2 - ISES Solar World Congress 2021 (SWC 2021)

Y2 - 25 October 2021 through 29 October 2021

ER -

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

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this