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A review of solar energy modeling techniques

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  • Khatib, Tamer
  • Mohamed, Azah
  • Sopian, K.

Abstract

Solar radiation data provide information on how much of the sun's energy strikes a surface at a location on the earth during a particular time period. These data are needed for effective research in solar-energy utilization. Due to the cost of and difficulty in solar radiation measurements and these data are not readily available, alternative ways of generating these data are needed. In this paper, a review is made on the solar energy modeling techniques which are classified based on the nature of the modeling technique. Linear, nonlinear, artificial intelligence models for solar energy prediction have been considered in this review. The outcome of the review showed that the sunshine ratio, ambient temperature and relative humidity are the most correlated coefficients to solar energy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:16:y:2012:i:5:p:2864-2869
    DOI: 10.1016/j.rser.2012.01.064
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    1. Tang, Runsheng & Wu, Tong, 2004. "Optimal tilt-angles for solar collectors used in China," Applied Energy, Elsevier, vol. 79(3), pages 239-248, November.
    2. Yakup, Mohd Azmi bin Hj Mohd & Malik, A.Q, 2001. "Optimum tilt angle and orientation for solar collector in Brunei Darussalam," Renewable Energy, Elsevier, vol. 24(2), pages 223-234.
    3. Benghanem, M., 2011. "Optimization of tilt angle for solar panel: Case study for Madinah, Saudi Arabia," Applied Energy, Elsevier, vol. 88(4), pages 1427-1433, April.
    4. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2006. "Computation of beam solar radiation at normal incidence using artificial neural network," Renewable Energy, Elsevier, vol. 31(10), pages 1483-1491.
    5. Zarzalejo, Luis F. & Ramirez, Lourdes & Polo, Jesus, 2005. "Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index," Energy, Elsevier, vol. 30(9), pages 1685-1697.
    6. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
    7. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    8. 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.
    9. Yohanna, Jonathan K. & Itodo, Isaac N. & Umogbai, Victor I., 2011. "A model for determining the global solar radiation for Makurdi, Nigeria," Renewable Energy, Elsevier, vol. 36(7), pages 1989-1992.
    10. Shariah, Adnan & Al-Akhras, M-Ali & Al-Omari, I.A., 2002. "Optimizing the tilt angle of solar collectors," Renewable Energy, Elsevier, vol. 26(4), pages 587-598.
    11. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    12. Ghosh, H.R. & Bhowmik, N.C. & Hussain, M., 2010. "Determining seasonal optimum tilt angles, solar radiations on variously oriented, single and double axis tracking surfaces at Dhaka," Renewable Energy, Elsevier, vol. 35(6), pages 1292-1297.
    13. Mellit, A. & Kalogirou, S.A. & Shaari, S. & Salhi, H. & Hadj Arab, A., 2008. "Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system," Renewable Energy, Elsevier, vol. 33(7), pages 1570-1590.
    14. 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.
    15. Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
    16. Topçu, S. & Dİlmaç, S. & Aslan, Z., 1995. "Study of hourly solar radiation data in Istanbul," Renewable Energy, Elsevier, vol. 6(2), pages 171-174.
    17. Janjai, S. & Praditwong, P. & Moonin, C., 1996. "A new model for computing monthly average daily diffuse radiation for Bangkok," Renewable Energy, Elsevier, vol. 9(1), pages 1283-1286.
    18. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    19. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    20. Sopian, Kamaruzzaman & Othman, Mohd.Yusof Hj., 1992. "Estimates of monthly average daily global solar radiation in Malaysia," Renewable Energy, Elsevier, vol. 2(3), pages 319-325.
    21. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
    22. Trabea, A.A, 2000. "Analysis of solar radiation measurements at Al-Arish area, North Sinai, Egypt," Renewable Energy, Elsevier, vol. 20(1), pages 109-125.
    23. Benghanem, Mohamed & Mellit, Adel, 2010. "Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia," Energy, Elsevier, vol. 35(9), pages 3751-3762.
    24. 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.
    25. Chineke, Theo Chidiezie, 2008. "Equations for estimating global solar radiation in data sparse regions," Renewable Energy, Elsevier, vol. 33(4), pages 827-831.
    26. Kacira, Murat & Simsek, Mehmet & Babur, Yunus & Demirkol, Sedat, 2004. "Determining optimum tilt angles and orientations of photovoltaic panels in Sanliurfa, Turkey," Renewable Energy, Elsevier, vol. 29(8), pages 1265-1275.
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