Solar resource classification in South Africa using a new index
This paper introduces a solar resource index that responds to site-specific sky conditions resulting from stochastic movement and evolution of clouds. The developed solar resource classification index called probability of persistence (POPD) had limited capabilities to distinguish persistent clear-sky conditions from persistent overcast-sky conditions. The metric proposed in this investigation, referred to as the solar utility index (SUI), seeks to extend the POPD index to a simple enough index that can singly discriminate different states of a solar resource. It gives a measure of the fractional time during which a solar resource exhibits predefined characteristics over a specific time period not exceeding the time interval between sunrise and sunset. These solar resource qualities, which are user-defined, measure: (1)Â the fluctuation characteristic of the solar resource magnitude, and (2) the solar resource diffuse and beam composition. Values of the indexes computed over daily time intervals of 7:00â€“17:00 apparent solar time were tested for their solar resource classification qualities. Five distinct classes using K-means clustering algorithm were identified for the solar radiation resource measured at eight stations in South Africa. The SUI was found to have superior solar resource discriminating and grouping abilities when compared with other indexes like POPD and fractal dimension.
2. Duffie, J. and Beckman, W. 2013. Solar engineering of thermal processes, fourth edition, John Wiley and sons.
3. Peled, A. and Appelbaum, J. 2013. Evaluation of solar radiation properties by statistical tools and wavelet analysis. Renewable Energy, 59: 30-38. http://dx.doi.org/10.1016/j.renene.2013.03.019.
4. Maafi, A. and Harrouni, S. 2003. Preliminary results of the fractal classification of daily solar irradiances. Solar Energy, 75: 53-61. http://dx.doi.org/ 10.1016/S0038-092X(03)00192-0.
5. Soubdhan, T., Emilion, R. and Rudy, C. 2009. Classification of daily solar radiation distributions using a mixture of dirichlet distributions. Solar Energy, 83: 1056-1063. http://dx.doi.org/10.1016/j.solener.2009.01.010.
6. GastÃ³n-Romeo, M., Leon, T., Mallor, F. and RamÃ-rez-Santigosa, L. 2011. A morphological clustering method for daily solar radiation curves. Solar Ener-gy, 85 : 1824-1836. http://dx.doi.org/10.1016/ j.solener.2011.04.023.
7. Stein, J. S., Hansen, C. W. and Reno, M. J. The vari-ability index: A new and novel metric for quantifying irradiance and pv output variability. World Renewa-ble Energy Forum, Denver, CO, 2012.
8. Kang, B. O. and Tam, K. 2013. A new characterization and classification method for daily sky condi-tions based on ground-based solar irradiance meas-urement data. Solar Energy, 94 :102-118. http://dx.doi.org/10.1016/j.solener.2013.04.007.
9. Perez, R., Kivalov, S., Schlemmer, J., Hemker Jr, K. and Hoff, T. 2011. Parameterization of site-specific short-term irradiance variability. Solar Energy, 85 : 1343-1353. http://dx.doi.org/10.1016/j.solener. 2011.03.016.
10. Ineichen, P. and Perez, R. 2002. A new airmass independent formulation for the linke turbidity coeffi-cient. Solar Energy, 73: 151-157. http://dx.doi.org/10.1016/S0038-092X(02)00045-2.
11. SNL. 2012. Pv_lib toolbox for matlab. Sandia Na-tional Laboratories. Available from: http://pvpmc.org/ pv-lib/ [Accessed: 02 January 2015].
12. Kalogirou, S. A. 2014. Chapter 11 â€“ designing and modeling solar energy systems. In: Kalogirou, S. A. (ed.) Solar energy engineering, second edition. Bos-ton: Academic Press, 583-699. http://dx.doi.org/ 10.1016/B978-0-12-397270-5.00011-X.
13. SAURAN. 2015. Southern african universities radi-ometric network. Available from: http://www.sauran.net/ [Accessed: 10 October 2015].
14. Brooks, M. J., du Clou, S., van Niekerk, W. L., Gau-chÃ©, P., Leonard, C., Mouzouris, M. J., Meyer, R., van der Westhuizen, N., van Dyk, E. E. and Vorster, F. J. 2015. Sauran: A new resource for solar radio-metric data in southern africa. Journal of Energy in Southern Africa, 26: 2-10.
15. Kipp and Zonen. 2017. Kipp & zonen pyranome-ters. Available from: http://www.kippzonen.com/ProductGroup/3/Pyranometers [Accessed: 17 May 2017].
16. Nabney, I. T. 2002. Netlab: Algorithms for pattern recognition. In: Singh, S. (ed.) Advances in pattern recognition. Great Britain: Springer.
17. Rousseeuw, P. J. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20: 53-65. http://dx.doi.org/10.1016/0377-0427(87)90125-7.
18. Climatemps. 2014. Sunshine & daylight hours in Durban, South Africa. Climatemps. Available from: http://www.durban.climatemps.com/sunlight.php [Accessed: 27 January 2016].
Copyright remains with the author(s).
Publishing rights remain with the author(s)
All articles published in JESA can be re-used under the following CC license: CC BY-SA Creative Commons Attribution-ShareAlike 4.0 International License.