skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Automatic Detection of Clear-Sky Periods From Irradiance Data

Abstract

Recent degradation studies have illustrated the importance of considering cloud cover when calculating degradation rates, finding more reliable values when the data are restricted to clear sky periods. Several automated methods of determining clear sky periods have been previously developed, but parameterizing and testing the models has been difficult. Here, we use clear sky classifications determined from satellite data to develop an algorithm that determines clear sky periods using only measured irradiance values and modeled clear sky irradiance as inputs. This approach is tested on global horizontal irradiance (GHI) data from ground collectors at six sites across the United States and compared against independent satellite-based classifications. First, 30 separate models were optimized on each individual site at GHI data intervals of 1, 5, 10, 15, and 30 min (sampled on the first minute of the interval). The models had an average F 0.5 score of 0.949 ± 0.035 on a holdout test set. Next, optimizations were performed by aggregating data from different locations at the same interval, yielding one model per data interval. This paper yielded an average F 0.5 of 0.946 ± 0.037. A final, 'universal' optimization that was trained on data from all sites at all intervals providedmore » an F 0.5 score of 0.943 ± 0.040. The optimizations all provide improvements on a prior, unoptimized clear sky detection algorithm that produces F 0.5 scores that average to 0.903 ± 0.067. Our paper indicates that a single algorithm can accurately classify clear sky periods across locations and data sampling intervals.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1543125
Alternate Identifier(s):
OSTI ID: 1656522
Report Number(s):
NREL/JA-5K00-73529
Journal ID: ISSN 2156-3381
Grant/Contract Number:  
AC36-08GO28308; AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Journal of Photovoltaics
Additional Journal Information:
Journal Volume: 9; Journal Issue: 4; Journal ID: ISSN 2156-3381
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; classification algorithms; solar energy

Citation Formats

Ellis, Benjamin H., Deceglie, Michael, and Jain, Anubhav. Automatic Detection of Clear-Sky Periods From Irradiance Data. United States: N. p., 2019. Web. doi:10.1109/JPHOTOV.2019.2914444.
Ellis, Benjamin H., Deceglie, Michael, & Jain, Anubhav. Automatic Detection of Clear-Sky Periods From Irradiance Data. United States. https://doi.org/10.1109/JPHOTOV.2019.2914444
Ellis, Benjamin H., Deceglie, Michael, and Jain, Anubhav. Fri . "Automatic Detection of Clear-Sky Periods From Irradiance Data". United States. https://doi.org/10.1109/JPHOTOV.2019.2914444. https://www.osti.gov/servlets/purl/1543125.
@article{osti_1543125,
title = {Automatic Detection of Clear-Sky Periods From Irradiance Data},
author = {Ellis, Benjamin H. and Deceglie, Michael and Jain, Anubhav},
abstractNote = {Recent degradation studies have illustrated the importance of considering cloud cover when calculating degradation rates, finding more reliable values when the data are restricted to clear sky periods. Several automated methods of determining clear sky periods have been previously developed, but parameterizing and testing the models has been difficult. Here, we use clear sky classifications determined from satellite data to develop an algorithm that determines clear sky periods using only measured irradiance values and modeled clear sky irradiance as inputs. This approach is tested on global horizontal irradiance (GHI) data from ground collectors at six sites across the United States and compared against independent satellite-based classifications. First, 30 separate models were optimized on each individual site at GHI data intervals of 1, 5, 10, 15, and 30 min (sampled on the first minute of the interval). The models had an average F 0.5 score of 0.949 ± 0.035 on a holdout test set. Next, optimizations were performed by aggregating data from different locations at the same interval, yielding one model per data interval. This paper yielded an average F 0.5 of 0.946 ± 0.037. A final, 'universal' optimization that was trained on data from all sites at all intervals provided an F 0.5 score of 0.943 ± 0.040. The optimizations all provide improvements on a prior, unoptimized clear sky detection algorithm that produces F 0.5 scores that average to 0.903 ± 0.067. Our paper indicates that a single algorithm can accurately classify clear sky periods across locations and data sampling intervals.},
doi = {10.1109/JPHOTOV.2019.2914444},
url = {https://www.osti.gov/biblio/1543125}, journal = {IEEE Journal of Photovoltaics},
issn = {2156-3381},
number = 4,
volume = 9,
place = {United States},
year = {2019},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Save / Share: