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The role of clouds in improving the regression model for hourly values of diffuse solar radiation


  • Furlan, Claudia
  • de Oliveira, Amauri Pereira
  • Soares, Jacyra
  • Codato, Georgia
  • Escobedo, João Francisco


The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R2=0.93 and RMSE=0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R2=0.92.

Suggested Citation

  • Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
  • Handle: RePEc:eee:appene:v:92:y:2012:i:c:p:240-254
    DOI: 10.1016/j.apenergy.2011.10.032

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    References listed on IDEAS

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    Cited by:

    1. Wang, Lunche & Gong, Wei & Ma, Yingying & Hu, Bo & Wang, Wenling & Zhang, Miao, 2013. "Analysis of ultraviolet radiation in Central China from observation and estimation," Energy, Elsevier, vol. 59(C), pages 764-774.
    2. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    3. Božnar, Marija Zlata & Grašič, Boštjan & Mlakar, Primož & Soares, Jacyra & de Oliveira, Amauri Pereira & Costa, Tássio Santos, 2015. "Radial frequency diagram (sunflower) for the analysis of diurnal cycle parameters: Solar energy application," Applied Energy, Elsevier, vol. 154(C), pages 592-602.
    4. Martin Hofmann & Gunther Seckmeyer, 2017. "A New Model for Estimating the Diffuse Fraction of Solar Irradiance for Photovoltaic System Simulations," Energies, MDPI, Open Access Journal, vol. 10(2), pages 1-21, February.
    5. repec:gam:jeners:v:10:y:2017:i:10:p:1587-:d:114699 is not listed on IDEAS
    6. Wang, Lunche & Gong, Wei & Li, Chen & Lin, Aiwen & Hu, Bo & Ma, Yingying, 2013. "Measurement and estimation of photosynthetically active radiation from 1961 to 2011 in Central China," Applied Energy, Elsevier, vol. 111(C), pages 1010-1017.
    7. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    8. 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.
    9. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    10. Kocifaj, Miroslav & Kómar, Ladislav, 2016. "Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation," Applied Energy, Elsevier, vol. 166(C), pages 117-127.
    11. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    12. Marques Filho, Edson P. & Oliveira, Amauri P. & Vita, Willian A. & Mesquita, Francisco L.L. & Codato, Georgia & Escobedo, João F. & Cassol, Mariana & França, José Ricardo A., 2016. "Global, diffuse and direct solar radiation at the surface in the city of Rio de Janeiro: Observational characterization and empirical modeling," Renewable Energy, Elsevier, vol. 91(C), pages 64-74.
    13. Pan, Tao & Wu, Shaohong & Dai, Erfu & Liu, Yujie, 2013. "Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China," Applied Energy, Elsevier, vol. 107(C), pages 384-393.
    14. repec:eee:rensus:v:77:y:2017:i:c:p:1326-1342 is not listed on IDEAS
    15. Mateos, D. & Antón, M. & Valenzuela, A. & Cazorla, A. & Olmo, F.J. & Alados-Arboledas, L., 2014. "Efficiency of clouds on shortwave radiation using experimental data," Applied Energy, Elsevier, vol. 113(C), pages 1216-1219.
    16. Badescu, Viorel & Dumitrescu, Alexandru, 2014. "Simple models to compute solar global irradiance from the CMSAF product Cloud Fractional Coverage," Renewable Energy, Elsevier, vol. 66(C), pages 118-131.
    17. repec:eee:energy:v:131:y:2017:i:c:p:149-164 is not listed on IDEAS
    18. Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
    19. repec:eee:rensus:v:78:y:2017:i:c:p:329-355 is not listed on IDEAS


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