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A critical review of the models used to estimate solar radiation

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  • Zhang, Jianyuan
  • Zhao, Li
  • Deng, Shuai
  • Xu, Weicong
  • Zhang, Ying

Abstract

Solar radiation data is critical to the design and operation of solar energy utilization systems, so a large number of models have been proposed and developed to estimate solar radiation in the past ten years. However, the performances of such models are controversial in different studies, and there is a lack of systematic comparison among them. In addition, few studies pay attention to the time scales and practicability of the models. This paper focuses on solving these questions through a critical literature review and the authors believe it can benefit researchers to perform further investigations about solar radiation. This paper reviews and compares the models from the points of view of time scale and estimation type for the first time. Furthermore, a large amount of data about the evaluation metrics (root mean square error and mean absolute percentage error) from different studies is summarized to clarify the performances of proposed models. The questions arising from the processing of source data are also carefully examined. This paper has presented a novel method to compare the estimation models and has provided a detailed analysis on available models. The results indicate that the sunshine duration fraction models and artificial neural networks have similar performances when used to estimate monthly average daily global radiation and daily global radiation, while more work is needed to study the estimation method on smaller time intervals and the mechanisms of atmospheric attenuation for solar radiation.

Suggested Citation

  • Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    • Handle: RePEc:eee:rensus:v:70:y:2017:i:c:p:314-329
    DOI: 10.1016/j.rser.2016.11.124
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    1. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    2. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2012. "A review of solar energy modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2864-2869.
    3. El-Sebaii, A.A. & Al-Ghamdi, A.A. & Al-Hazmi, F.S. & Faidah, Adel S., 2009. "Estimation of global solar radiation on horizontal surfaces in Jeddah, Saudi Arabia," Energy Policy, Elsevier, vol. 37(9), pages 3645-3649, September.
    4. Qin, Jun & Chen, Zhuoqi & Yang, Kun & Liang, Shunlin & Tang, Wenjun, 2011. "Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products," Applied Energy, Elsevier, vol. 88(7), pages 2480-2489, July.
    5. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
    6. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
    7. Almorox, J. & Hontoria, C. & Benito, M., 2011. "Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain)," Applied Energy, Elsevier, vol. 88(5), pages 1703-1709, May.
    8. 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.
    9. Almorox, J. & Benito, M. & Hontoria, C., 2005. "Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain," Renewable Energy, Elsevier, vol. 30(6), pages 931-936.
    10. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    11. Almorox, Javier & Bocco, Mónica & Willington, Enrique, 2013. "Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina," Renewable Energy, Elsevier, vol. 60(C), pages 382-387.
    12. Toğrul, İnci Türk & Toğrul, Hasan & Evin, Dugyu, 2000. "Estimation of monthly global solar radiation from sunshine duration measurement in Elaziğ," Renewable Energy, Elsevier, vol. 19(4), pages 587-595.
    13. Bakirci, Kadir, 2009. "Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey," Energy, Elsevier, vol. 34(4), pages 485-501.
    14. Lu, Ning & Qin, Jun & Yang, Kun & Sun, Jiulin, 2011. "A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data," Energy, Elsevier, vol. 36(5), pages 3179-3188.
    15. Kaplanis, S. & Kaplani, E., 2010. "Stochastic prediction of hourly global solar radiation for Patra, Greece," Applied Energy, Elsevier, vol. 87(12), pages 3748-3758, December.
    16. Bakirci, Kadir, 2009. "Models of solar radiation with hours of bright sunshine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2580-2588, December.
    17. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    18. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
    19. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    20. Korachagaon, Iranna & Bapat, V.N., 2012. "General formula for the estimation of global solar radiation on earth’s surface around the globe," Renewable Energy, Elsevier, vol. 41(C), pages 394-400.
    21. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    22. Wan, Kevin K.W. & Tang, H.L. & Yang, Liu & Lam, Joseph C., 2008. "An analysis of thermal and solar zone radiation models using an Angstrom–Prescott equation and artificial neural networks," Energy, Elsevier, vol. 33(7), pages 1115-1127.
    23. Katiyar, A.K. & Pandey, Chanchal Kumar, 2010. "Simple correlation for estimating the global solar radiation on horizontal surfaces in India," Energy, Elsevier, vol. 35(12), pages 5043-5048.
    24. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    25. 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.
    26. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    27. El Mghouchi, Y. & Ajzoul, T. & El Bouardi, A., 2016. "Prediction of daily solar radiation intensity by day of the year in twenty-four cities of Morocco," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 823-831.
    28. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
    29. Zang, Haixiang & Xu, Qingshan & Bian, Haihong, 2012. "Generation of typical solar radiation data for different climates of China," Energy, Elsevier, vol. 38(1), pages 236-248.
    30. Rahimikhoob, Ali, 2010. "Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment," Renewable Energy, Elsevier, vol. 35(9), pages 2131-2135.
    31. Khorasanizadeh, H. & Mohammadi, K., 2013. "Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran," Energy, Elsevier, vol. 51(C), pages 257-266.
    32. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    33. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    34. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong & Zhao, Liang, 2011. "Global solar radiation estimation with sunshine duration in Tibet, China," Renewable Energy, Elsevier, vol. 36(11), pages 3141-3145.
    35. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong, 2010. "Estimating daily global solar radiation by day of year in China," Applied Energy, Elsevier, vol. 87(10), pages 3011-3017, October.
    36. Akarslan, Emre & Hocaoğlu, Fatih Onur & Edizkan, Rifat, 2014. "A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting," Energy, Elsevier, vol. 73(C), pages 978-986.
    37. Janjai, S. & Pankaew, P. & Laksanaboonsong, J. & Kitichantaropas, P., 2011. "Estimation of solar radiation over Cambodia from long-term satellite data," Renewable Energy, Elsevier, vol. 36(4), pages 1214-1220.
    38. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    39. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    40. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.
    41. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
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