References
- Almorox, J., M. Benito, and C. Hontoria. 2005. Estimation of monthly Angstrom-Prescott equation coefficients from measured daily data in Toledo, Spain. Renewable Energy 30(6):931–36.
- Al Shamisi, M.H., A. Assi, and H. Hejase. 2013. Artificial neural network modeling of global solar radiation in Al Ain city, UAE. International Journal of Green Energy 10(5):443–56.
- Assi, A., M. Jama, and M. Al-Shamisi. 2012. Prediction of global solar radiation in Abu Dhabi. ISRN Renewable Energy Article ID 328237, 10 pp.
- Chen, J.-L., H.-B. Liu, W. Wu, and D.-T. Xie. 2011. Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study. Renewable Energy 36(1),413–20.
- Deng, F., G. Su, C., Liu, and Z., Wang. 2010. Prediction of solar radiation resources in China using the LS-SVM algorithms. The 2nd International Conference on Computer and Automation Engineering (ICCAE). Singapore, February 26–28.
- Elgin, K., and A. Hepbasli. 2002. Prediction of solar radiation parameters through clearness index for Izmir. Turkey. Energy Sources 24(8):773–85.
- Elizondo, D., G. Hoogenboom, and R. McClendon. 1996. Development of a neural network to predict daily solar radiation. Agricultural and Forest Meteorology 71:115–32.
- Hejase, H., and A. Assi. 2012. Time-series regression modeling of daily average global solar radiation in Al Ain city, UAE. ISRN Renewable Energy, Article ID 412471, 11 pp.
- Hontoria, L., J. Aguilera, J. Riesco, and P.J. Zufiria. 2001. Recurrent neural supervised models for generating solar radiation. Journal of Intelligent and Robotic System 31:201–21.
- Lopez, G., F.J. Batlles, and J. Tovar-Pescador. 2005, July. Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy 30(9):1675–84.
- Mellit, A., A.M. Pavan, and M. Benghanem. 2013. Least squares support vector machine for short-term prediction of meteorological time series. Theoretical and Applied Climatology111:297–307.
- Mohandes, M. 2012. Modeling global solar radiation using Particle Swarm Optimization (PSO). Solar Energy 86(11):3137–45.
- Rehman, S., and M. Mohandes. 2009. Estimation of diffuse fraction of solar radiation using artificial neural networks. Energy Sources Part A: Recovery, Utilization, and Environmental Effects 31:974–84.
- Rehman, S., and M. Mohandes, 2012. Splitting global solar radiation into diffuse and direct normal fraction. Energy Sources Journal, A: Recovery, Utilization, and Environmental Effects 34(14):1326–36.
- Sözen, A., E. Arcaklıoˇlu, and M. ÖZalp. 2004. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Conversion and Management 45:3033–52.
- Vapnik, V.N. 1995. The nature of statistical learning theory. New York, NY: Springer-Verlag
- Wu, W., and H.-B. Liu. 2012. Assessment of monthly solar radiation estimates using support vector machines and air temperatures. International Journal of Climatology 32:274–85.
![Supercharge Your Next Research Paper: Research and write your next paper with Jenni AI.]({"type":"image" , "src" : "/sda/112446/MPU-L1EB.png"})