Investigating diffuse irradiance variation under different cloud conditions in Durban, using k-means clustering
Keywords:cloud cover patterns; stratocumulus; altocumulus; cirrus
Diffuse irradiance is important for the operation of solar-powered devices such as photovoltaics, so it is important to analyse its behaviour under different sky conditions. The primary cause of short-term irradiance variability is clouds. One approach to analyse the diffuse irradiance variation is to use cluster analysis to group together days experiencing similar cloud patterns. A study was carried out to examine the application of k-means clustering to daily cloud data in Durban, South Africa (29.87 °S; 30.98 °E), which revealed four distinct day-time cloud cover (CC) patterns classified as Class I, II, III and IV, corresponding to cloudy, sunny, or a combination of the two. Diffuse irradiance was then correlated with each of the classes to establish corresponding diurnal irradiance patterns and the associated temporal variation. Class I had highest diffuse irradiance variation, followed by Classes III, IV and II. To further investigate the local cloud dynamics, cloud types were also analysed for Classes I−IV. It was found that stratocumulus (low cloud category); altocumulus translucidus, castellanus and altocumulus (middle cloud category); and cirrus fibrates and spissatus (high cloud category), were the most frequently occurring cloud types within the different classes. This study contributes to the understanding of the diurnal diffuse irradiance patterns under the four most frequently occurring CC conditions in Durban. Overall, knowledge of these CC and associated diffuse irradiance patterns is useful for solar plant operators to manage plant output where, depending on the CC condition, the use of back-up devices may be increased or reduced accordingly.
Accuweather, 2019. https://www.accuweather.com/en/za/durban/305605/hourly-weather-forecast/305605?hour=81. Accessed on 14 August 2019.
Azimi, R. Ghayekhloo, M. and Ghofrani, M. 2016. A hybrid method based on a new clustering technique and multi-layer perceptron neural networks for hourly solar radiation forecasting. Energy Conversion and Management 118: 331-344. http://dx.doi.org/10.1016/j.enconman.2016.04.009.
Bae, K.Y., Jang, H.S. and Sung, D.K. 2017. Hourly solar irradiance prediction based on support vector machine and its error analysis. IEEE Transactions on Power Systems 32(2): 935-945. http://dx.doi.org/10.1109/TPWRS.2016.2569608.
Benmouiza, K. and Cheknane, A. 2013. Forecasting hourly solar radiation using hybrid k-means and non-linear au-toregressive neural network models. Energy Conversion and Management 75: 561-569. http://dx.doi.org/10.1016/j.enconman.2013.07.003.
Chaturvedi, D.K. and Isha, I. 2016. Solar power forecasting: a review. International Journal of Computer Applications 145(6): 28-50. https://doi.org/10.5120/ijca2016910728.
Chu, H.J., Liau, C.J., Lin, C.H. and Su, B.S. 2012. Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region. Expert Systems with Applications 39: 9451-9457. https://doi.org/10.1016/j.eswa.2012.02.114.
Gomasathit, T. 2013. Cloud coverage identification using satellite and k-mean clustering algorithm. Journal of Global Research in Computer Science 4(7): 33-37.
Govender, P., Brooks, M.J. and Matthews, A.P. 2018. Cluster analysis for classification and forecasting of solar irradi-ance in Durban, South Africa. Journal of Energy Southern Africa 29(1): 59-71. https://dx.doi.org/10.17159/2413-3051/2017/v29i2a4338.
Halkidi, M., Batistakis, Y. and Vazirgiannis, M. 2001. On clustering validation techniques. Journal of Intelligent Infor-mation Systems 17:107-145. https://doi.org/10.1023/A:1012801612483.
Hartigan, J.A., Wong, M.A. 1979. Algorithm AS 136: A k-means clustering algorithm. Applied Statistics 28: 100-108. https://dx.doi.org/10.2307/2346830.
Heinle, A., Macke, A., Srivastav, A. 2010. Automatic cloud classification of whole sky images. Atmospheric Measure-ment Techniques 3: 557-567. https://dx.doi.org/10.5194/amt-3-557-2010.
Jain, A.K. 2010. Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31: 651-666. https://doi.org/10.1016/j.patrec.2009.09.011.
Jury, M.R. 2013. Climate trends in southern Africa. South African Journal of Science 109 (1/2): 1-11. https://dx.doi.org/10.1590/sajs.2013/980.
Jury, M.R. 2017. Climate trends across South Africa since 1980. Water SA 43(4): 297-307.
Kottek, M., Grieser, J., Beck, C., Rudolf, B. and Rubel, F. 2006. World Map of the Koppen-Gieger climate classification updated. Meteorologische Zeitschrift 15: 259-263. https://dx.doi.org/10.1127/0941-2948/2006/0130.
Kostornaya, A.A., Saprykin, E.I., Zakhvatov, M.G. and Tokareva, Y.V. 2017. A method of cloud detection from satellite data. Russian Meteorology and Hydrology 42(12): 753-758. https://doi.org/10.3103/S1068373917120020.
Kruger, A.C. 2006. Observed trends in daily precipitation indices in South Africa: 1910-2004.
International Journal of Climatology 26: 2275-2285. https://dx.doi.org/10.1002/joc.1368.
Kruger, A.C. and Sekele, S.S. 2013. Trends in extreme temperature indices in South Africa: 1962-2009. International Journal of Climatology 33: 661-676. https://dx.doi.org/10.1002/joc.3455.
Kritzinger K. Policy brief: Solar Photovoltaic Technologies. Available from: https://www.crses.sun.ac.za/files/research/publications/popular-media-and-policy-brief/PV%20Policy%20Brief%20Dec%202017.pdf. Accessed on 17 April 2019.
Lleti, R., Ortiz, M., Sarabia, L. and Sanchez, M. 2004. Selecting variables for k-means cluster analysis by using a genet-ic algorithm that optimises the silhouettes. Analytica Chimica Acta 515: 87-100. https://doi.org/10.1016/j.aca.2003.12.020.
MacQueen, J.B. Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berke-ley Symposium on Mathematical Statistics and Probability; University of California, 1967, 281-297.
Marquez, R. and Coimbra, C.F.M. 2013. Intra-hour DNI forecasting methodology based on cloud tracking image analysis. Solar Energy 91: 327-36. https://doi.org/10.1016/j.solener.2012.09.018.
MATLAB and Statistics Toolbox Release 2018b. The MathWorks Inc., Natick, Massachusetts, United States.
https://www.mathworks.com/help/stats/k-means-clustering.html. Accessed on 13 August 2019.
Quante, M. 2004. The role of clouds in the climate system. Journal de Physique IV France 121: 61-86. https://dx.doi.org/10.1051/jp4:2004121003.
Rousseeuw, P. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computa-tional and Applied Mathematics 20: 53-65. https://doi.org/10.1016/0377-0427(87)90125-7.
World Meteorological Organization (WMO) 1975. International Cloud Atlas: Manual on the observation of clouds and other meteors (WMO-No. 407). http://www.wmo.int/pages/governance/policy/tech_regu_en.html. Accessed on 20 July 2017.
Xuejin, S., Lei, L. and Shijun, Z. 2011. Whole sky infrared remote sensing of cloud. Procedia Earth and Planetary Science 2: 278-283. https://dx.doi.org/10.1016/j.proeps.2011.09.044
Zawilska, E. and Brooks M.J. 2011. An assessment of the solar resource for Durban, South Africa. Renewable Energy 36: 3433-3438. https://dx.doi.org/10.1016/j.renene.2011.05.023.
How to Cite
Copyright (c) 2019 Paulene Govender, Venkataraman Sivakumar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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.