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Clear-sky detection for PV degradation analysis using multiple regression

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  • Jordan, Dirk C.
  • Hansen, Clifford

Abstract

A method is presented to detect clear-sky periods for plane-of-array irradiance time-averaged data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data averaging intervals. Moreover, our new method detects clear periods in plane-of-array data, which is novel. The new method is developed by applying a Design of Experiment approach to optimize the parameters used in the Reno method, and Monte Carlo simulations are used to understand the robustness of the found parameters. Clear-sky detection accuracy is compared among four methods: the Reno method, the default clear-sky filter in RdTools, the Ellis method, and the method outlined in this work, using a hand-labeled two-year data set of 1-min plane-of-array irradiance for a fixed tilt system. The RdTools clear-sky filter is marred by excessive false positives. The other methods all perform well at 1-min data intervals; the method developed here provides more accurate detection at longer data averaging intervals. We show that the parameters are directly linked to the data frequency in the hope that these input variables may not have to be optimized for every data frequency and location. However, only a single fixed system in one location was carefully examined. Finally, we illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

Suggested Citation

  • Jordan, Dirk C. & Hansen, Clifford, 2023. "Clear-sky detection for PV degradation analysis using multiple regression," Renewable Energy, Elsevier, vol. 209(C), pages 393-400.
  • Handle: RePEc:eee:renene:v:209:y:2023:i:c:p:393-400
    DOI: 10.1016/j.renene.2023.04.035
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    References listed on IDEAS

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    1. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    2. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    3. Sina Herceg & Ismail Kaaya & Julián Ascencio-Vásquez & Marie Fischer & Karl-Anders Weiß & Liselotte Schebek, 2022. "The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    4. Reno, Matthew J. & Hansen, Clifford W., 2016. "Identification of periods of clear sky irradiance in time series of GHI measurements," Renewable Energy, Elsevier, vol. 90(C), pages 520-531.
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