Analysing Concentrating Photovoltaics Technology Through the Use of Emerging Pattern Mining

  • A. M. García-Vico
  • J. Montes
  • J. Aguilera
  • C. J. CarmonaEmail author
  • M. J. del Jesus
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)


The search of emerging patterns pursues the description of a problem through the obtaining of trends in the time, or characterisation of differences between classes or group of variables. This contribution presents an application to a real-world problem related to the photovoltaic technology through the algorithm EvAEP. Specifically, the algorithm is an evolutionary fuzzy system for emerging pattern mining applied to a problem of concentrating photovoltaic technology which is focused on the generation of electricity reducing the associated costs. Emerging patterns have discovered relevant information for the experts when the maximum power is reached for the cells of concentrating photovoltaic.


Emerging pattern mining Concentrating photovoltaics Evolutionary fuzzy system Supervised descriptive rule discovery 



This work was supported by the Spanish Science and Innovation Department under project ENE2009-08302, by the Department of Science and Innovation of the Regional Government of Andalucia under project P09-TEP-5045, and by Spanish Ministry of Economy and Competitiveness under project TIN2015-68454-R (FEDER Founds).


  1. 1.
    Almonacid, F., Pérez-Higueras, P., Fernández, E., Rodrigo, P.: Relation between the cell temperature of a hcpv module and atmospheric parameters. Sol. Energy Mater. Sol. Cells 105, 322–327 (2012)CrossRefGoogle Scholar
  2. 2.
    Antón, I., Martínez, M., Rubio, F., Núñez, R., Herrero, R., Domínguez, C., Victoria, M., Askins, S., Sala, G.: Power rating of CPV systems based on spectrally corrected DNI, vol. 1477, pp. 331–335 (2012)Google Scholar
  3. 3.
    Carmona, C.J., González, P., del Jesus, M.J., Herrera, F.: NMEEF-SD: non-dominated multi-objective evolutionary algorithm for extracting fuzzy rules in subgroup discovery. IEEE Trans. Fuzzy Syst. 18(5), 958–970 (2010)CrossRefGoogle Scholar
  4. 4.
    Carmona, C.J., González, P., del Jesus, M.J., Herrera, F.: Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms. WIREs Data Min. Knowl. Disc. 4(2), 87–103 (2014)CrossRefGoogle Scholar
  5. 5.
    Carmona, C.J., González, P., García-Domingo, B., del Jesus, M.J., Aguilera, J.: MEFES: an evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology. Knowl. Based Syst. 54, 73–85 (2013)CrossRefGoogle Scholar
  6. 6.
    Carmona, C.J., Pulgar-Rubio, F.J., García-Vico, A.M., González, P., del Jesus, M.J.: Análisis descriptivo mediante aprendizaje supervisado basado en patrones emergentes. In: Proceedings of the VII Simposio Teoría y Aplicaciones de Minería de Datos, pp. 685–694 (2015)Google Scholar
  7. 7.
    Castro, M., Domínguez, C., Núez, R., Antón, I., Sala, G., A. K.: Detailed effects of wind on the field performance of a 50 kw CPV demonstration plant. In: AIP Conference Proceedings, vol. 1556, pp. 256–260 (2013)Google Scholar
  8. 8.
    Dong, G.Z., Li, J.Y.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43–52. ACM Press (1999)Google Scholar
  9. 9.
    Dong, G.Z., Li, J.Y.: Mining border descriptions of emerging patterns from dataset pairs. Knowl. Inf. Syst. 8(2), 178–202 (2005)CrossRefGoogle Scholar
  10. 10.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer, Heidelberg (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms 2, pp. 187–202. Kaufmann Publishers (1993)Google Scholar
  12. 12.
    Helmers, H., Schachtner, M., Bett, A.: Influence of temperature and irradiance on triple-junction solar subcells. Sol. Energy Mater. Sol. Cells 116, 144–152 (2013)CrossRefGoogle Scholar
  13. 13.
    Herrera, F.: Genetic fuzzy systems: taxomony, current research trends and prospects. Evol. Intel. 1, 27–46 (2008)CrossRefGoogle Scholar
  14. 14.
    Herrera, F., Carmona, C.J., González, P., del Jesus, M.J.: An overview on Subgroup Discovery: Foundations and Applications. Knowl. Inf. Syst. 29(3), 495–525 (2011)CrossRefGoogle Scholar
  15. 15.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  16. 16.
    Hüllermeier, E.: Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst. 156(3), 387–406 (2005)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kinsey, G., Hebert, P., Barbour, K., Krut, D., Cotal, H., Sherif, R.: Concentrator multijunction solar cell characteristics under variable intensity and temperature. Prog. Photovoltaics Res. Appl. 16(6), 503–508 (2008)CrossRefGoogle Scholar
  18. 18.
    Kralj-Novak, P., Lavrac, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of constrast set, emerging pateern and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)zbMATHGoogle Scholar
  19. 19.
    Li, J.Y., Dong, G.Z., Ramamohanarao, K., Wong, L.: DeEPs: a new instance-based lazy discovery and classification system. Mach. Learn. 54(2), 99–124 (2004)CrossRefzbMATHGoogle Scholar
  20. 20.
    Lin, C.-K., Fang, J.-Y.: Analysis of structural deformation and concentrator misalignment in a roll-tilt solar tracker. In: AIP Conference Proceedings, vol. 1556, pp. 210–213 (2013)Google Scholar
  21. 21.
    Peharz, G., Ferrer Rodríguez, J., Siefer, G., Bett, A.: Investigations on the temperature dependence of CPV modules equipped with triple-junction solar cells. Prog. Photovoltaics Res. Appl. 19(1), 54–60 (2011)CrossRefGoogle Scholar
  22. 22.
    Venturini, G.: SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 280–296. Springer, Heidelberg (1993). doi: 10.1007/3-540-56602-3_142 CrossRefGoogle Scholar
  23. 23.
    Zadeh, L.A.: The concept of a linguistic variable and its applications toapproximate reasoning. Parts I, II, III. Inform. Sci. 8-9, 199–249, 301–357, 43–80 (1975)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • A. M. García-Vico
    • 1
  • J. Montes
    • 2
  • J. Aguilera
    • 2
  • C. J. Carmona
    • 3
    Email author
  • M. J. del Jesus
    • 1
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Electronics and Automatization EngineeringUniversity of JaénJaénSpain
  3. 3.Department of Civil EngineeringUniversity of BurgosBurgosSpain

Personalised recommendations