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Analysing Concentrating Photovoltaics Technology Through the Use of Emerging Pattern Mining

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International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 (SOCO 2016, CISIS 2016, ICEUTE 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

Abstract

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.

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Notes

  1. 1.

    http://www.nrel.gov/ncpv/images/efficiency_chart.jpg.

References

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Dong, G.Z., Li, J.Y.: Mining border descriptions of emerging patterns from dataset pairs. Knowl. Inf. Syst. 8(2), 178–202 (2005)

    Article  Google Scholar 

  10. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer, Heidelberg (2003)

    Book  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  13. Herrera, F.: Genetic fuzzy systems: taxomony, current research trends and prospects. Evol. Intel. 1, 27–46 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  16. Hüllermeier, E.: Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst. 156(3), 387–406 (2005)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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 

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Acknowledgment

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).

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Jaén, 23071, Jaén, Spain

    A. M. García-Vico & M. J. del Jesus

  2. Department of Electronics and Automatization Engineering, University of Jaén, 23071, Jaén, Spain

    J. Montes & J. Aguilera

  3. Department of Civil Engineering, University of Burgos, 09006, Burgos, Spain

    C. J. Carmona

Authors
  1. A. M. García-Vico
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  2. J. Montes
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  3. J. Aguilera
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  4. C. J. Carmona
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  5. M. J. del Jesus
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Corresponding author

Correspondence to C. J. Carmona .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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García-Vico, A.M., Montes, J., Aguilera, J., Carmona, C.J., del Jesus, M.J. (2017). Analysing Concentrating Photovoltaics Technology Through the Use of Emerging Pattern Mining. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

  • eBook Packages: EngineeringEngineering (R0)

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