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Modeling the luminous efficacy of direct and diffuse solar radiation using information on cloud, aerosol and water vapor in the tropics

Author

Listed:
  • Janjai, S.
  • Prathumsit, J.
  • Buntoung, S.
  • Wattan, R.
  • Pattarapanitchai, S.
  • Masiri, I.

Abstract

This paper presents luminous efficacy models for direct and diffuse solar irradiance using information on cloud, aerosol and water vapor in the tropics. The model is based on five years (2007–2011) of diffuse illuminance and irradiance measurements and two years of direct illuminance and irradiance measurements, April 2010–March 2012. Data are taken at four solar radiation monitoring stations in Thailand, specifically Chiang Mai (18.78 °N, 98.98 °E) in the Northern region, Ubon Ratchathani (15.25 °N, 104.87 °E) in the Northeastern region, Nakhon Pathom (13.82 °N, 100.04 °E) in the Central region and Songkhla (7.20 °N, 100.60 °E) in the Southern region. The models express luminous efficacy as functions of the aerosol optical depth and precipitable water, obtained from the AERONET network, and a cloud index for hourly time scales derived from the MTSAT-1R satellite. The model performance is good when validated against independent data from these stations. Root mean square differences (RMSD) of 9.7% and 6.8% for direct normal efficacy and diffuse efficacy, respectively are obtained. The models compared favorably with most existing models when tested against these independent data.

Suggested Citation

  • Janjai, S. & Prathumsit, J. & Buntoung, S. & Wattan, R. & Pattarapanitchai, S. & Masiri, I., 2014. "Modeling the luminous efficacy of direct and diffuse solar radiation using information on cloud, aerosol and water vapor in the tropics," Renewable Energy, Elsevier, vol. 66(C), pages 111-117.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:111-117
    DOI: 10.1016/j.renene.2013.11.070
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    References listed on IDEAS

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    Cited by:

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    2. Ramedani, Zeynab & Omid, Mahmoud & Keyhani, Alireza & Shamshirband, Shahaboddin & Khoshnevisan, Benyamin, 2014. "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1005-1011.
    3. Purohit, Ishan & Purohit, Pallav, 2015. "Inter-comparability of solar radiation databases in Indian context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 735-747.
    4. Li, Danny H.W. & Aghimien, Emmanuel I. & Tsang, Ernest K.W., 2022. "Application of artificial neural networks in horizontal luminous efficacy modeling," Renewable Energy, Elsevier, vol. 197(C), pages 864-878.
    5. Cheng, Xinghong & Ye, Dong & Shen, Yanbo & Li, Deping & Feng, Jinming, 2022. "Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China," Renewable Energy, Elsevier, vol. 196(C), pages 358-365.

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