International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Fuzzy Partitioning of Photovoltaic Solar Power Patterns

  • Munshi, Amr (Computer Engineering Department, Umm Al-Qura University)
  • Received : 2022.05.05
  • Published : 2022.05.30
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Abstract

Photovoltaic systems provide a reliable green energy solution. The sustainability and low-maintenance of Photovoltaic systems motivate the integration of Photovoltaic systems into the electrical grid and further contribute to a greener environment, as the system does not cause any pollution or emissions. Developing methodologies based on machine learning techniques to assist in reducing the burden of studies related to integrating Photovoltaic systems into the electric grid are of interest. This research aims to develop a methodology based on a unsupervised machine learning algorithm that can reduce the burden of extensive studies and simulations related to the integration of Photovoltaic systems into the electrical grid.

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References

  1. G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, "Customer characterization options for improving the tariff offer," IEEE Trans. Power Syst., vol. 18, no. 1, pp. 381-387, Feb. 2003.
  2. V. Figueiredo, F. Rodrigues, Z. Vale, and J. B. Gouveia, "An electric energy consumer characterization framework based on data mining techniques," IEEE Trans. Power Syst., vol. 20, no. 2, pp. 596-602, May 2005. https://doi.org/10.1109/TPWRS.2005.846234
  3. D. Gerbec, S. Gasperic, I. Smon, and F. Gubina, "Determining the load profiles of consumers based on fuzzy logic and probability neural networks," Proc. Inst. Elect. Eng., Gener. Transm., Distrib., vol. 151, no. 3, pp. 395-400, 2004. https://doi.org/10.1049/ip-gtd:20040472
  4. G. Chicco, R. Napoli, F. Piglione, P. Postolache, M. Scutariu, and C. Toader, "Load pattern-based classification of electricity customers," IEEE Trans. Power Syst., vol. 19, no. 2, pp. 1232-1239, May 2004.
  5. G. Chicco, R. Napoli, and F. Piglione, "Comparisons among clustering techniques for electricity customer classification," IEEE Trans. Power Syst., vol. 21, no. 2, pp. 933-940, May 2006.
  6. S. V. Verdu, M. O. Garcia, C. Senabre, A. G. Marin, and F. J. G. Franco, "Classification, filtering, and identification of electrical customer load patterns through the use of Self-Organizing Maps," IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1672-1682, Nov. 2006. https://doi.org/10.1109/TPWRS.2006.881133
  7. J. Tsekouras, N. D. Hatziargyriou, and E. N. Dialynas, "Two-stage pattern recognition of load curves for classification of electricity customers," IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1120-1128, August. 2007. https://doi.org/10.1109/TPWRS.2007.901287
  8. G. Chicco, O. M. Ionel, and R. Porumb, "Electrical load pattern grouping based on centroid model with ant colony clustering," IEEE Power Syst., vol. 28, no. 2, pp. 1706-1715, May 2013. https://doi.org/10.1109/TPWRS.2012.2220159
  9. G. Chicco, R. Napoli, and F. Piglione, "Load pattern clustering for short-term load forecasting of anomalous days," Proc. IEEE PowerTech Conf., Porto, 10-13 Sept 2001, vol. 2.
  10. A. Gabaldon, A. Guillamon, M. C. Ruiz, S. Valero, C. Alvarez, M. Ortiz, and C. Senabre, "Development of a methodology for clustering electricity-price series to improve customer response initiatives," IET Gener., Transm., Distrib., vol. 4, no. 6, pp. 706-715, 2010. https://doi.org/10.1049/iet-gtd.2009.0112
  11. G. J. Tsekouras, C. A. Anastasopoulos, F. D. Kanellos, V. T. Kontargyri, I. S. Karanasiou, A. D. Salis, and N. E. Mastorakis, "A demand side management program of vanadium redox energy storage system for an interconnected power system," Proc. WSEAS EPESE, Corfu Island, Greece, 2008.
  12. G. J. Tsekouras, F. D. Kanellos, V.T. Kontargyri, I. S. Karanasiou, A. D. Salis, and N. E. Mastorakis, "A new classification pattern recognition methodology for power system typical load profiles," WSEAS Trans. Circuits and Systems, vol. 12, no. 7, pp. 1090-1104, 2008.
  13. G. Vijayakumar, M. Kummert, S. Klein, W. Beckman, "Analysis of short-term solar radiation data," Solar Energy, vol.79, pp. 495-504, 2005. https://doi.org/10.1016/j.solener.2004.12.005
  14. C. Craggs, E. M. Conway and N. M. Pearsall, "Statistical investigation of the optimal averaging time for solar irradiance on horizontal and vertical surfaces in the UK," Solar Energy, vol.68, pp. 179-187, 2000. https://doi.org/10.1016/S0038-092X(99)00063-8
  15. R. Babuska, Fuzzy and Neural Control DISC Course Notes, Delft University of Technology, Netherlands, 2009.
  16. A. V. Gawand, P. Lokhande, S. Daware, and U. Kulkarni, "Image Segmentation for Nature Images using K-Mean and Fuzzy C-Mean," International Journal of Computer Applications, 2011.
  17. R. Xu, and D. Wunsch, "Survey of Clustering Algorithms," IEEE Trans. Neural Networks, vol. 16, no. 3, pp. 645-678, 2005. https://doi.org/10.1109/TNN.2005.845141
  18. G. Kopp, and J. L. Lean, "A new, lower value of total solar irradiance: evidence and climate significance," Geophys. Res. Lett., vol. 38, 2011.
  19. G. M. Masters, Renewable and Efficient Electric Power Systems, John Wiley & Son, 2004, pp. 505-531.
  20. G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, "Customer characterization options for improving the tariff offer," IEEE Trans. Power Syst., vol. 18, no. 1, pp. 381-387, Feb. 2003.
  21. Solar Radiation Research Laboratory (BMS) available online at: http://www.nrel.gov/midc/srrl_bms. Retrieved January 2014.
  22. H. Mori, and M. Takahashi, "Application of preconditioned generalized radial basis function network to prediction of photovoltaic power generation," Proc. IEEE ISGT, pp.1-6, 14-17 Oct. 2012.