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Evaluation and performance comparison of different models for the estimation of solar radiation

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  • Teke, Ahmet
  • Yıldırım, H. Başak
  • Çelik, Özgür

Abstract

The rapid depletion of energy resources, increasing energy demand and degeneration of ecological values need an urgent solution in this age. Solar energy as the most important energy resource has become part of the solution to the world’s energy challenges. Solar radiation data that provides the information on how much energy strikes to the earth is needed for utilization, planning and designing of solar power plants. The measurement of solar radiation data is generally available in some specific areas due to difficulty in solar radiation measurements in terms of its initial and maintenance costs. Therefore, solar energy modelling techniques are becoming more and more important due to the increasing need for the design, performance evaluation and improvement of the solar energy applications. The primary aim of this paper is to overview solar radiation modelling techniques to identify optimum models available and to classify research fields in the literature. In this paper, the modelling techniques, data information, accuracy tests of models used in around 90 papers were reviewed and the most accurate models were suggested.

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  • 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.
    • Handle: RePEc:eee:rensus:v:50:y:2015:i:c:p:1097-1107
    DOI: 10.1016/j.rser.2015.05.049
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    as
    1. 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.
    2. Wu, Wei & Tang, Xiao-Ping & Yang, Chao & Guo, Nai-Jia & Liu, Hong-Bin, 2013. "Spatial estimation of monthly mean daily sunshine hours and solar radiation across mainland China," Renewable Energy, Elsevier, vol. 57(C), pages 546-553.
    3. Sabziparvar, Ali A. & Shetaee, H., 2007. "Estimation of global solar radiation in arid and semi-arid climates of East and West Iran," Energy, Elsevier, vol. 32(5), pages 649-655.
    4. Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    5. 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.
    6. Rusen, Selmin Ener & Hammer, Annette & Akinoglu, Bulent G., 2013. "Coupling satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface," Renewable Energy, Elsevier, vol. 55(C), pages 212-219.
    7. 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.
    8. 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.
    9. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    10. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    11. 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.
    12. 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.
    13. Antonanzas-Torres, F. & Sanz-Garcia, A. & Martínez-de-Pisón, F.J. & Perpiñán-Lamigueiro, O., 2013. "Evaluation and improvement of empirical models of global solar irradiation: Case study northern Spain," Renewable Energy, Elsevier, vol. 60(C), pages 604-614.
    14. El Ouderni, Ahmed Ridha & Maatallah, Taher & El Alimi, Souheil & Ben Nassrallah, Sassi, 2013. "Experimental assessment of the solar energy potential in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 155-168.
    15. Chen, S.X. & Gooi, H.B. & Wang, M.Q., 2013. "Solar radiation forecast based on fuzzy logic and neural networks," Renewable Energy, Elsevier, vol. 60(C), pages 195-201.
    16. Lee, Kwanho & Yoo, Hochun & Levermore, Geoff J., 2013. "Quality control and estimation hourly solar irradiation on inclined surfaces in South Korea," Renewable Energy, Elsevier, vol. 57(C), pages 190-199.
    17. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    18. 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.
    19. Ener Rusen, Selmin & Hammer, Annette & Akinoglu, Bulent G., 2013. "Estimation of daily global solar irradiation by coupling ground measurements of bright sunshine hours to satellite imagery," Energy, Elsevier, vol. 58(C), pages 417-425.
    20. 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.
    21. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2013. "Hybrid methodology for hourly global radiation forecasting in Mediterranean area," Renewable Energy, Elsevier, vol. 53(C), pages 1-11.
    22. Bulut, Hüsamettin & Büyükalaca, Orhan, 2007. "Simple model for the generation of daily global solar-radiation data in Turkey," Applied Energy, Elsevier, vol. 84(5), pages 477-491, May.
    23. 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.
    24. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    25. 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.
    26. 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.
    27. 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.
    28. Jiang, Yingni, 2008. "Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models," Energy Policy, Elsevier, vol. 36(10), pages 3833-3837, October.
    29. 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.
    30. 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.
    31. Toğrul, Inci Turk & Toğrul, Hasan & Evin, Duygu, 2000. "Estimation of global solar radiation under clear sky radiation in Turkey," Renewable Energy, Elsevier, vol. 21(2), pages 271-287.
    32. Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C.G., 2008. "Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques," Renewable Energy, Elsevier, vol. 33(8), pages 1796-1803.
    33. Notton, Gilles & Paoli, Christophe & Ivanova, Liliana & Vasileva, Siyana & Nivet, Marie Laure, 2013. "Neural network approach to estimate 10-min solar global irradiation values on tilted planes," Renewable Energy, Elsevier, vol. 50(C), pages 576-584.
    34. Mostafavi, Elham Sadat & Ramiyani, Sara Saeidi & Sarvar, Rahim & Moud, Hashem Izadi & Mousavi, Seyyed Mohammad, 2013. "A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran," Energy, Elsevier, vol. 49(C), pages 204-210.
    35. 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.
    36. Jiang, Yingni, 2009. "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, Elsevier, vol. 34(9), pages 1276-1283.
    37. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
    38. López, Gabriel & Rubio, Miguel Angel & Batlles, Francisco J., 2000. "Estimation of hourly direct normal from measured global solar irradiance in Spain," Renewable Energy, Elsevier, vol. 21(2), pages 175-186.
    39. Demain, Colienne & Journée, Michel & Bertrand, Cédric, 2013. "Evaluation of different models to estimate the global solar radiation on inclined surfaces," Renewable Energy, Elsevier, vol. 50(C), pages 710-721.
    40. 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.
    41. 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.
    42. 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.
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