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Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea

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  • Hyun-Jin Lee

    (School of Mechanical Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02727, Korea)

  • Shin-Young Kim

    (New and Renewable Energy Resource Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Chang-Yeol Yun

    (New and Renewable Energy Resource Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

Abstract

Reliable solar radiation data are important for energy simulations in buildings and solar energy systems. Although direct normal irradiance (DNI) is required for simulations, in addition to global horizontal irradiance (GHI), a lack of DNI measurement data is quite often due to high cost and maintenance. Solar radiation models are widely used in order to overcome the limitation, but only a few studies have been devoted to solar radiation data and modeling in Korea. This study investigates the most suitable solar radiation model that converts GHI into DNI for Korea, using measurement data of the city of Daejeon from 2007 to 2009. After ten existing models were evaluated, the Reindl-2 model was selected as the best. A new model was developed for further improvement, and it substantially decreased estimation errors compared to the ten investigated models. The new model was also evaluated for nine major cities other than Daejeon from the standpoint of typical meteorological year (TMY) data, and consistent evaluation results confirmed that the new model is reliably applicable across Korea.

Suggested Citation

  • Hyun-Jin Lee & Shin-Young Kim & Chang-Yeol Yun, 2017. "Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea," Energies, MDPI, vol. 10(5), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:594-:d:97257
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    References listed on IDEAS

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    Cited by:

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    2. Soumya Das & Marc G. Genton & Yasser M. Alshehri & Georgiy L. Stenchikov, 2021. "A cyclostationary model for temporal forecasting and simulation of solar global horizontal irradiance," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    3. Ramadhan, Raden A.A. & Heatubun, Yosca R.J. & Tan, Sek F. & Lee, Hyun-Jin, 2021. "Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power," Renewable Energy, Elsevier, vol. 178(C), pages 1006-1019.
    4. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
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    6. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.
    7. Zhiyong Tian & Bengt Perers & Simon Furbo & Jianhua Fan & Jie Deng & Janne Dragsted, 2018. "A Comprehensive Approach for Modelling Horizontal Diffuse Radiation, Direct Normal Irradiance and Total Tilted Solar Radiation Based on Global Radiation under Danish Climate Conditions," Energies, MDPI, vol. 11(5), pages 1-19, May.
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