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Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction

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  • Starke, Allan R.
  • Lemos, Leonardo F.L.
  • Boland, John
  • Cardemil, José M.
  • Colle, Sergio

Abstract

For design and simulation of solar energy systems, quality information about all components of solar irradiance is crucial. In cases when only global irradiance measurements are available, separation models are a useful method to estimate DNI and diffuse irradiation. Several of such models have been developed since the 1960s, most of them aiming to deliver estimates in hourly resolution. For higher data resolution, such as in minute data, those models are not able to describe fast transient and cloud enhancement phenomena commonly observed in data with smaller time-steps. This paper proposes an adaptation of the BRL separation model, making it capable of delivering more precise irradiance estimates for higher resolution data. Two models result from this adaptation: one for Brazil and other for Australia. The proposed models yield a more precise DNI and diffuse fraction estimates to their respective countries, compared to other separation models commonly used in the technical literature. For example, using the recommended Combined Performance Index (CPI) as a single statistical indicator, the proposed model yields DNI estimates with CPI from 230 to 350% for Australia, and from 270 to 800% for Brazil, while the Engerer model, recently recommended as a ‘‘quasi-universal” 1-min separation model, yields DNI estimates with CPI from 500 to 700% for Australia, and from 600 to 1800% for Brazil.

Suggested Citation

  • Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
    • Handle: RePEc:eee:renene:v:125:y:2018:i:c:p:472-484
    DOI: 10.1016/j.renene.2018.02.107
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    References listed on IDEAS

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    1. Elminir, Hamdy K. & Azzam, Yosry A. & Younes, Farag I., 2007. "Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models," Energy, Elsevier, vol. 32(8), pages 1513-1523.
    2. Boland, John & Huang, Jing & Ridley, Barbara, 2013. "Decomposing global solar radiation into its direct and diffuse components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 749-756.
    3. Gueymard, Christian A., 2014. "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1024-1034.
    4. Lemos, Leonardo F.L. & Starke, Allan R. & Boland, John & Cardemil, José M. & Machado, Rubinei D. & Colle, Sergio, 2017. "Assessment of solar radiation components in Brazil using the BRL model," Renewable Energy, Elsevier, vol. 108(C), pages 569-580.
    5. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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    Cited by:

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