Vol. 3, No. 3 (Summer 2016) 21-30   

Link: http://www.jree.ir/Vol3/No3/3.pdf
Downloaded Downloaded: 66   Viewed Viewed: 241

  Introducing the best model for predicting global solar radiation in Iran using empirical models and artificial neural network
H. Ghasemi Mobtaker, Y. Ajabshirchi, S. F. Ranjbar, M. Matloobi and M. Taki
( Received: January 10, 2016 – Accepted: September 04, 2016 )

Abstract    Precise knowledge of the amount of global solar radiation plays an important role in designing of a renewable energy systems. In this study, using long-term meteorological data, 19 empirical models were tested for prediction of monthly mean daily global solar radiation in Tabriz. Also various artificial neural networks (ANN) models were designed for the comparison with the empirical models. For this purpose, the meteorological data recorded by Iran Meteorological Office (1992–2013) was used. These data included: monthly mean daily sunshine duration, monthly mean ambient temperature, monthly mean maximum and minimum ambient temperature and monthly mean relative humidity. The results showed that the yearly average of solar radiation in the region was 16.37 MJ m-2 day-1. Among the empirical models, the best result was acquired for model (19) with correlation coefficient of 0.9663. Results also showed that the ANN model trained with total meteorological data in input layer produces better results than the others. RMSE and r for this model were 1.0800 MJ m-2 and 0.9714, respectively. Comparison between the two models demonstrated that modeling of monthly mean daily global solar radiation through the use of the ANN technique shows better estimates rather than the empirical models.


Keywords    Solar energy, Meteorological data, Sunshine hour, Prediction, Artificial neural networks.


References    Abdalla, Y. A. G. (1994). New correlation of global solar radiation with meteorological parameters for Bahrain. International Journal of Solar Energy 16: 111–20. Adaramola, M. S. (2012). Estimating global solar radiation using common meteorological data in Akure, Nigeria. Renewable Energy 47: 38–44. Ajayi, O. O. O. D. Ohijeagbon, C. E. Nwadialo, and O. Olasope. (2014). New model to estimate daily global solar radiation over Nigeria. Sustainable Energy Technologies and Assessments 5: 28–36. Allen, R. (1997). Self calibrating method for estimating solar radiation from air temperature. Journal of Hydrologic Engineering (2): 56–67. Duffie, J. A. and W. A. Beckman. (2013). Solar Engineering of Thermal Processes, fourth edition. John Wiley & Son, New Jersey. El-Metwally, M. (2005). Sunshine and global solar radiation estimation at different sites in Egypt. Journal of Atmospheric and Solar-Terrestrial Physics 67: 1331–42. El-Sebaii, A. A., A. A. Al-Ghamdi, F. S. Al-Hazmi, and A. S. Faidah. (2009). Estimation of global solar Radiation on horizontal surfaces in Jeddah, Saudi Arabia. Energy Policy 37: 3645–9. El-Sebaii, A. A. Al-Hazmi F. S. A. A. Al-Ghamdi, S. J. Yaghmour. (2010). Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia. Applied Energy 87: 568–76. Farhadi-Bansouleh, B. M. A, Sharifi, and H. Van Keulen. (2009). Sensitivity analysis of performance of crop growth simulation models to daily solar radiation estimation methods in Iran. Energy Conversion and Management 50: 2826–36. Hargreaves, G. L. G. H. Hargreaves, and P. Riley. (1985). Irrigation water requirement for the Senegal River Basin. Journal of Irrigation and Drainage Engineering 111(3): 265–75. Hasni, A. A. Sehli, B. Draoui, A. Bassou and B. Amieur. (2012). Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria. Energy Procedia 18: 531–7. Jafarpur, K. and M. A. Yaghoubi. (1989). Solar radiation for Shiraz, Iran. Solar and Wind Technology 1989 6(2): 177–9. Jiang, Y. (2009). Estimation of monthly mean daily diffuse radiation in China. Applied energy 86(9): 1458–64. Jin, Z. Wu. Yezheng, and Y. Gang, (2005). General formula for estimation of monthly average daily global solar radiation in China. Energy Conversion and Management 46: 257–68. Kaushika, N. D. R. K. Tomar and S. C. Kaushik. (2014). Artificial neural network model based on interrelationship of direct, diffuse and global solar radiations. Solar Energy 103: 327–42. Khorasanizadeh, H., and K. Mohammadi. (2013). Prediction of daily global solar radiation by day of the year in four cities located in the sunny regions of Iran. Energy Conversion and Management 76: 385–92. Khorasanizadeh, H. K. Mohammadi and A. Mostafaeipour. (2014). Establishing a diffuse solar radiation model for determining the optimum tilt angle of solar surfaces in Tabass, Iran. Energy Conversion and Management 78: 805–14. Li, H. W. Ma, Y. Lian and X. Wang. (2010). Estimating daily global solar radiation by day of year in China. Applied Energy 87: 3011–7. Li, M. F. L. Fan, H. B. Liu, P. T. Guo, and W. Wu. (2013). A general model for estimation of daily global solar radiation using air temperatures and site geographic parameters in South west China. Journal of Atmospheric and Solar-Terrestrial Physics 92: 145–50. Li, M. F. X. P. Tang, W. Wu, and H. B. Liu. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management 70: 139–48. Linares-Rodriguez, A. J. A. Ruiz-Arias, and D. Pozo-Vazquez. (2013). An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images. Energy 61: 636–45. Ogelman, H. A. Ecevit, and E. Tasdemiroglu. (1984). A new method for estimating solar radiation from bright sunshine data. Solar Energy 33: 619–25. Ozgoren, M. M. Bilgili, and B. Sahin. (2012). Estimation of global solar radiation using ANN over Turkey. Expert Systems with Applications 39: 5043–51. Rahimikhoob, A. (2010). Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renewable Energy 35: 2131–5. Ramedani, Z. M. Omid, and A. Keyhani. (2013). Modeling Solar Energy Potential in a Tehran Province Using Artificial Neural Networks. International Journal of Green Energy 10(4): 427–41. Ramedani, Z. M. Omid, A. Keyhani. B. Khoshnevisan, and H. Saboohi. (2015). A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran. Solar Energy 109: 135–43. Robaa, S. M. (2009). Validation of the existing models for estimating global solar radiation over Egypt. Energy Conversion and Management 50: 184–93. Sabziparvara, A. A. and H. Shetaee. (2007). Estimation of global solar radiation in arid and semi-arid climates of East and West Iran. Energy 32: 649–55. Sen, Z. (2007). Simple nonlinear solar irradiation estimation model. Renewable Energy 32(2): 342–50. Shamim, M. A. R. Remesan, M. Bray, and D. Han. (2015). An improved technique for global solar radiation estimation using numerical weather prediction. Journal of Atmospheric and Solar-Terrestrial Physics 129: 13–22. Taki, M. Y. Ajabshirchi, and A. Mahmoudi. (2012a). Prediction of output energy for wheat production using artificial neural networks in Esfahan province of Iran. Journal of Agricultural Science and Technology 8(4): 1229–42. Taki, M. A. Mahmoudi, H. G. Mobtaker, and H. Rahbari. (2012b). Energy consumption and modeling of output energy with multilayer feed-forward neural network for corn silage in Iran. Agricultural Engineering International: CIGR Journal. 14(4): 93–101. Togrul, I. T. and H. Togrul. (2002). Global solar radiation over Turkey: comparison of predicted and measured data. Renewable Energy 25: 55–67. Waewsak, J. C. Chancham, M. Mani, and Y. Gagnon. (2014). Estimation of monthly mean maily global solar radiation over Bangkok, Thailand using Artificial Neural Networks. Energy Procedia 57: 1160–8. Zarzo, M. and P. Marti. (2011). Modelling the variability of solar radiation data among weather stations by means of principal component analysis. Applied Energy 88: 2775–84. Ziuku, S. L. Seyitini, B. Mapurisa, D. Chikodzi, and K. Koen van. (2014). Potential of Concentrated Solar Power (CSP) in Zimbabwe. Energy for Sustainable Development 23: 220–7.

Download PDF