Automatic Detection of Clear-Sky Periods Using Ground and Satellite Based Solar Resource Data

Ellis, Benjamin H.; Deceglie, Michael G; Jain, Anubhav

doi:10.1109/PVSC.2018.8547877

Title: Automatic Detection of Clear-Sky Periods Using Ground and Satellite Based Solar Resource Data

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Abstract

Solar resource availability and variability are important aspects of monitoring performance of photovoltaic installations. For example, recent degradation studies have highlighted the importance of considering cloud cover when calculation degradation rates. With this in mind, we present a method for optimizing clear sky detection algorithms given only modeled clear sky irradiance and ground-measured irradiance values. This method is tested on global horizontal irradiance (GHI) data from ground collectors at six sites across the US and was trained against clear sky classifications determined from satellite data. Thirty models were optimized on each individual site at GHI data frequencies of 1, 5, 10, 15, and 30 minutes. The models had an average F 0.5 score of 0.945 +/- .021 on a holdout test set. In comparison, the un-optimized clear sky detection algorithm produced F 0.5 score that averaged to 0.707 +/- 0.187.

Authors:

Ellis, Benjamin H. ^[1]; Deceglie, Michael G ^[2]; Jain, Anubhav ^[1]

Lawrence Berkeley National Laboratory
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Publication Date:: 2018-11-29

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1507672

Report Number(s):: NREL/CP-5K00-73686

DOE Contract Number:: AC36-08GO28308

Resource Type:: Conference

Resource Relation:: Conference: Presented at the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC), 10-15 June 2018, Waikoloa Village, Hawaii

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; classification algorithms; solar energy

Citation Formats


                    Ellis, Benjamin H., Deceglie, Michael G, and Jain, Anubhav. Automatic Detection of Clear-Sky Periods Using Ground and Satellite Based Solar Resource Data.  United States: N. p., 2018. 
        Web.  doi:10.1109/PVSC.2018.8547877.

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                    Ellis, Benjamin H., Deceglie, Michael G, & Jain, Anubhav. Automatic Detection of Clear-Sky Periods Using Ground and Satellite Based Solar Resource Data.  United States.  https://doi.org/10.1109/PVSC.2018.8547877

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                    Ellis, Benjamin H., Deceglie, Michael G, and Jain, Anubhav. 2018.  
        "Automatic Detection of Clear-Sky Periods Using Ground and Satellite Based Solar Resource Data".  United States.  https://doi.org/10.1109/PVSC.2018.8547877.

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@article{osti_1507672,

  title        = {Automatic Detection of Clear-Sky Periods Using Ground and Satellite Based Solar Resource Data},

  author       = {Ellis, Benjamin H. and Deceglie, Michael G and Jain, Anubhav},

  abstractNote = {Solar resource availability and variability are important aspects of monitoring performance of photovoltaic installations. For example, recent degradation studies have highlighted the importance of considering cloud cover when calculation degradation rates. With this in mind, we present a method for optimizing clear sky detection algorithms given only modeled clear sky irradiance and ground-measured irradiance values. This method is tested on global horizontal irradiance (GHI) data from ground collectors at six sites across the US and was trained against clear sky classifications determined from satellite data. Thirty models were optimized on each individual site at GHI data frequencies of 1, 5, 10, 15, and 30 minutes. The models had an average F 0.5 score of 0.945 +/- .021 on a holdout test set. In comparison, the un-optimized clear sky detection algorithm produced F 0.5 score that averaged to 0.707 +/- 0.187.},

  doi          = {10.1109/PVSC.2018.8547877},

  url          = {https://www.osti.gov/biblio/1507672},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2018},

  month        = {11}

}

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https://doi.org/10.1109/PVSC.2018.8547877

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