Automated construction of clear-sky dictionary from all-sky imager data
Abstract
All-sky imagers (ASIs) have significant promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.
- Authors:
-
[2];
[2]
- Univ. of Colorado, Boulder, CO (United States)
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Electricity (OE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- OSTI Identifier:
- 1755700
- Alternate Identifier(s):
- OSTI ID: 1810965
- Report Number(s):
- NREL/JA-5D00-76692
Journal ID: ISSN 0038-092X; MainId:9353;UUID:a8a5cb5e-101f-4d68-a49a-e35badba4fcf;MainAdminID:19010
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Solar Energy
- Additional Journal Information:
- Journal Volume: 212; Journal ID: ISSN 0038-092X
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; all-sky imager; clear-sky dictionary; cloud detection; digital image processing; red-to-blue ratio; short-term solar irradiance forecasting
Citation Formats
Shaffery, Peter, Habte, Aron, Netto, Marcos, Andreas, Afshin, and Krishnan, Venkat. Automated construction of clear-sky dictionary from all-sky imager data. United States: N. p., 2020.
Web. doi:10.1016/j.solener.2020.10.052.
Shaffery, Peter, Habte, Aron, Netto, Marcos, Andreas, Afshin, & Krishnan, Venkat. Automated construction of clear-sky dictionary from all-sky imager data. United States. https://doi.org/10.1016/j.solener.2020.10.052
Shaffery, Peter, Habte, Aron, Netto, Marcos, Andreas, Afshin, and Krishnan, Venkat. Tue .
"Automated construction of clear-sky dictionary from all-sky imager data". United States. https://doi.org/10.1016/j.solener.2020.10.052. https://www.osti.gov/servlets/purl/1755700.
@article{osti_1755700,
title = {Automated construction of clear-sky dictionary from all-sky imager data},
author = {Shaffery, Peter and Habte, Aron and Netto, Marcos and Andreas, Afshin and Krishnan, Venkat},
abstractNote = {All-sky imagers (ASIs) have significant promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.},
doi = {10.1016/j.solener.2020.10.052},
journal = {Solar Energy},
number = ,
volume = 212,
place = {United States},
year = {Tue Nov 10 00:00:00 EST 2020},
month = {Tue Nov 10 00:00:00 EST 2020}
}
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