Skip to main content

Fuzzy Modelling of Diffuse Solar Radiation

  • Conference paper
  • First Online:
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

Included in the following conference series:

  • 1560 Accesses

Abstract

Modelling the diffuse solar radiation is a topic in many scientific and research papers, but most of the presented models are conventional, approximate and constrained to a certain geographic region, lacking the universality. Furthermore, many of these mathematical models are polynomials of appropriate degree, and/or consist of a set of expressions derived for specific intervals of their parameters. This paper presents two unconventional models of the diffuse solar radiation built on fuzzy logic. First, a position fuzzy model is discussed, and then a position-gradient fuzzy model is given. To identify these models, a modification of the algorithm presented by Sugeno and Yasukawa is applied. The diffuse solar radiation is modelled with sufficient climatic data including total solar radiation, clearness index, total sky cover, precipitation water, relative humidity, etc. The presented models offer an alternative unconventional approach to modelling of diffuse solar radiation and they have shown very good performance and certain advantage compared to other existing models through simulation. There are, of course, other unconventional techniques for modelling the diffuse solar radiation, but the most remarkable about these models is that they successfully process many different input parameters and choose the ones that most significantly influence the model output.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Collares-Pereira, M., Rabl, A.: The average distribution of solar radiation-correlations between diffuse and hemispherical and between daily and hourly insolation values. Sol. Energy 22, 155–164 (1979). doi:10.1016/0038-092X(79)90100-2

    Article  Google Scholar 

  2. Erbs, D.G., Klein, S.A., Duffie, J.A.: Estimation of the diffuse radiation fraction of hourly, daily and monthly-average global radiation. Sol. Energy 28, 293–302 (1982). doi:10.1016/0038-092X(82)90302-4

    Article  Google Scholar 

  3. Iqbal, M.: Prediction of hourly diffuse solar radiation from measured hourly global radiation on a horizontal surface. Sol. Energy 24, 491–503 (1980). doi:10.1016/0038-092X(80)90317-5

    Article  Google Scholar 

  4. Liu, B.Y.H., Jordan, R.C.: The interrelationship and characteristic distribution of direct, diffuse, and total solar radiation. Sol. Energy 4(3), 1–19 (1960). doi:10.1016/0038-092X(60)90062-1

    Article  Google Scholar 

  5. Orgill, J.F., Hollands, K.G.T.: Correlation equation for hourly diffuse radiation on a horizontal surface. Sol. Energy 19(4), 357–359 (1977). doi:10.1016/0038-092X(77)90006-8

    Article  Google Scholar 

  6. Reindl, D.T., Beckman, W.A., Duffie, J.A.: Diffuse fraction correlations. Sol. Energy 45, 1–7 (1990). doi:10.1016/0038-092X(90)90060-P

    Article  Google Scholar 

  7. Skartveit, A., Olseth, J.A.: A model for the diffuse fraction of hourly global radiation. Sol. Energy 38, 271–274 (1987). doi:10.1016/0038-092X(87)90049-1

    Article  Google Scholar 

  8. Garrison, J.D.: A study of the division of global irradiance into direct and diffuse irradiance at thirty-three US cities. Sol. Energy 35, 341–351 (1985). doi:10.1016/0038-092X(85)90142-2

    Article  Google Scholar 

  9. Hay, J.E.: A revised method for determining the direct and diffuse components of the total shortwave radiation. Atmos. Q. Publ. Can. Meteorol. Soc. 14, 278–287 (1976). doi:10.1080/00046973.1976.9648423

    Google Scholar 

  10. Wesley, M.L., Lipschutz, R.C.: A method for estimating hourly averages of diffuse and direct solar radiation under a layer of scattered clouds. Sol. Energy 18, 467–473 (1976). doi:10.1016/0038-092X(76)90013-X

    Article  Google Scholar 

  11. Choudhury, N.K.D.: Solar radiation in New Delhi. Sol. Energy 7, 44–52 (1963). doi:10.1016/0038-092X(63)900004-5

    Article  Google Scholar 

  12. Page, J.K.: The estimation of monthly mean values of daily total short wave radiation on vertical and inclined surfaces from sunshine records for latitudes 40N–40S. In: Proceedings of the UN Conference on New Sources of Energy, vol. 4, pp. 378–389 (1964)

    Google Scholar 

  13. Ruth, D.W., Chant, R.E.: The relationship of diffuse radiation to total radiation in Canada. Sol. Energy (US) 18, 153–154 (1976). doi:10.1016/0038-092X(76)90049-9

    Article  Google Scholar 

  14. Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1, 7–33 (1993). doi:10.1109/TFUZZ.1993.390281

    Article  Google Scholar 

  15. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, Boston (1981)

    Book  MATH  Google Scholar 

  16. Dunn, J.C.: Well separated cluster and optimal fuzzy partitioning. J. Cybern. 4, 95–104 (1974)

    Article  Google Scholar 

  17. Fukuyama, Y., Sugeno, M.: A new method of choosing the number of clusters for the fuzzy c-means method. In: Proceedings of 5th Fuzzy System Symposium, pp. 247–250 (1989)

    Google Scholar 

  18. Gomez, A.F., Delgado, M., Vila, M.A.: About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets Syst. 106, 179–188 (1999). doi:10.1016/S016T-0114(97)00276-5

    Article  Google Scholar 

  19. Celikyilmaz, A., Turksen, I.B.: Enhanced fuzzy system models with improved fuzzy clustering algorithm. IEEE Trans. Fuzzy Syst. 16, 779–794 (2008). doi:10.1109/TFUZZ.2007.905919

    Article  Google Scholar 

  20. Chiu, S.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994). doi:10.3233/IFS-1994-2306

    Article  Google Scholar 

  21. Filev, D.: Fuzzy modelling of complex systems. Int. J. Approx. Reason. 5(3), 281–290 (1991). doi:10.1016/0888-613X(91)90013-C

    Article  MATH  Google Scholar 

  22. Kim, E., Park, M., Ji, S., Park, M.: A new approach to fuzzy modeling. IEEE Trans. Fuzzy Syst. 5, 328–337 (1997). doi:10.1109/91.618271

    Article  Google Scholar 

  23. Pedrycz, W.: An identification algorithm in fuzzy relational systems. Fuzzy Sets Syst. 13, 153–167 (1984). doi:10.1016/0165-0114(84)90015-0

    Article  MATH  Google Scholar 

  24. Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28, 15–33 (1988). doi:10.1016/0165-0114(84)90113-3

    Article  MathSciNet  MATH  Google Scholar 

  25. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985). doi:10.1109/TSMC.1985.6313399

    Article  MATH  Google Scholar 

  26. Xu, C.W., Yong, Z.: Fuzzy model identification and self-learning for dynamic systems. IEEE Trans. Syst. Man Cybern. 17, 683–689 (1987)

    Article  MATH  Google Scholar 

  27. Yager, R.R., Filev, D.P.: Generation of fuzzy rules by mountain clustering. J. Intell. Fuzzy Syst. 2, 209–219 (1994). doi:10.3233/IFS-1994-2301

    Google Scholar 

  28. Renewable Resource Data Center (RReDC): The National Renewable Energy Laboratory (NREL), U.S. Department of Energy. The National Solar Radiation Data Base (NSRDB) 1961–1990. http://rredc.nrel.gov/solar/old_data/nsrdb/. Accessed 6 May 2011

  29. Ihara, J.: Group method of data handling towards a modelling of complex systems IV. Syst. Control 24, 158–168 (1980)

    Google Scholar 

  30. Lazarevska, E., Trpovski, J.: A modification of the famous fuzzy model by Sugeno and Yasukawa. In: Proceedings of the International Symposium on Applied Automatic Systems AAS 2000, Ohrid, Macedonia, pp. 31–35 (2000)

    Google Scholar 

  31. Tikk, D., Birό, G., Kόczy, L.T., Gedeon, T.D., Wong, K.W.: Notes on Sugeno and Yasukawa’s fuzzy modeling approach. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference 2001, Vancuver, BC, 25–28 July 2001, vol. 5, pp. 2836–2841 (2001). doi:10.1109/NAFIPS.2001.943676

  32. Wong, K.W., Koczy, L.T., Chong, A., Tikk, D.: Improvement of the cluster searching algorithm in Sugeno and Yasukawa’s qualitative modeling approach. In: Reusch, B. (ed.) Computational Intelligence. Theory and Applications: International Conference, Proceedings of 7th Fuzzy Days Dortmund, Germany, 1–3 October 2001. Lecture Notes in Computer Science, pp. 536–549. Springer, London (2001)

    Google Scholar 

  33. Tikk, D., Birό, G., Kόczy, L.T., Gedeon, T.D., Wong, K.W.: Improvements and critique on Sugeno’s and Yasukawa’s qualitative modeling. IEEE Trans. Fuzzy Syst. 10(5), 596–606 (2002)

    Article  Google Scholar 

  34. Hadad, A.H., Ghidary, S.S., Bahrami, S., Shahbazi, S.: A modification of Sugeno-Yasukawa modeler to improve structure identification phase. ACSE J. 6(3), 33–40 (2006)

    Google Scholar 

  35. Hadad, A.H., Ghidary, S.S., Shouraki, S.B.: An improvement in Sugeno-Yasukawa modeler. In: International Conference on Computational Intelligence for Modeling, Control and Automation CIMCA 2006 and International Conference of Intelligent Agents, Web Technologies and Internet Commerce IAWTIC 2006, Sydney, NSW, 28 November–01 Dec 2006, p. 239 (2006). doi:10.1109/CIMCA.2006.42

  36. Hadad, A.H., Gedeon, T., Shahbazi, S., Bahrami, S.: A modified version of Sugeno-Yasukawa modeler. In: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, 9–11 March 2008, pp. 852–856 (2008)

    Google Scholar 

  37. Lazarevska, E.: Comparison of different unconventional approaches to modeling diffuse solar radiation based on fuzzy logic and neural network techniques (unpublished)

    Google Scholar 

Download references

Acknowledgements

The author gratefully acknowledges that the models built and discussed within this paper are based on the available data obtained from the National Renewable Energy Laboratory (NREL) web-site. NREL is operated by the Alliance for Sustainable Energy (LLC) for the US Department of Energy, and the used data is archived in The National Solar Radiation Data Base (NSRDB) 1961–1990.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elizabeta Lazarevska .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Lazarevska, E. (2018). Fuzzy Modelling of Diffuse Solar Radiation. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56994-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics