Skip to main content
SpringerLink
Log in

Power Curve Estimation of Wind Farms with Imprecise Data by Fuzzy Quadratic Programming

  • Chapter
  • First Online:
Computational Intelligence Methodologies Applied to Sustainable Development Goals

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1036))

  • 178 Accesses

Abstract

Quadratic programming can be applied in a variety of the real-world problems. As ambiguity and vagueness are natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using Fuzzy Quadratic Programming (FQP) problems. This way of problem modeling is applied to an increasing variety of practical fields especially those with renewable energy generation. In this work, we present a parametric approach that solve quadratic programming problems under different kind of uncertainties in its data. In order to show the efficiency of this approach, it is used in a fuzzy regression analysis problem, which try to find out a relationship between variables, i.e, to describe how a dependent variable is related with independent variables. Here, we consider regression analysis with imprecise parameters which are natural in real-life situations.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Ammar, E., Khalifa, H.A.: Fuzzy portfolio optimization a quadratic programming approach. Chaos Solitons Fractals 18, 1045–1054 (2003)

    Article  MathSciNet  Google Scholar 

  2. Bardossy, A.: Note on fuzzy regression. Fuzzy Sets Syst 37, 65–75 (1990)

    Article  MathSciNet  Google Scholar 

  3. Bector, C.R., Chandra, S.: Fuzzy mathematical programming and fuzzy matrix games. In: Volume 169 of Studies in Fuzziness and Soft Computing. Springer, Berlin (2005)

    Google Scholar 

  4. Bortolan, G., Degani, R.: A review of some methods for ranking fuzzy subsets. Fuzzy Sets Syst 15, 1–19 (1985)

    Article  MathSciNet  Google Scholar 

  5. Cruz, C., Silva, R.C., Verdegay, J.L.: Extending and relating different approaches for solving fuzzy quadratic problems. Fuzzy Optim. Decis. Making 10, 193–210 (2011)

    Article  MathSciNet  Google Scholar 

  6. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, LTD, Chichester (2001)

    MATH  Google Scholar 

  7. Delgado, M., Verdegay, J.L., Vila, M.A.: Imprecise costs in mathematical programming problems. Control Cybern. 16(2), 113–121 (1987)

    MathSciNet  MATH  Google Scholar 

  8. Delgado, M., Verdegay, J.L., Vila, M.A.: Relating different approaches to solve linear programming problems with imprecise costs. Fuzzy Sets Syst. 37, 33–42 (1990)

    Article  MathSciNet  Google Scholar 

  9. Diamond, P.: Fuzzy least squares. Inform. Sci. 46, 141–157 (1988)

    Article  MathSciNet  Google Scholar 

  10. Donoso, S., Marín, N., Vila, M.A.: Fuzzy regression with quadratic programming: an application to financial data. Lect. Notes Comput. Sci. 4224, 1304–1311 (2006)

    Article  Google Scholar 

  11. Jiménez, F., Cadenas, J.M., Sánchez, G., Gómez-Skarmeta, A.F., Verdegay, J.L.: Multi-objective evolutionary computation and fuzzy optimization. Int. J. Approx. Reason. 43(1), 59–75 (2006)

    Article  MathSciNet  Google Scholar 

  12. Klir, G.J.: Where do we stand on measures of uncertainty, ambiguity, fuzziness, and the like. Fuzzy Sets Syst. 24, 141–160 (1987)

    Article  MathSciNet  Google Scholar 

  13. Lee, G., Ding, Y., Genton, M., Xie, L.: Power curve estimation with multivariate environmental factors for inland and offshore wind farms. J. Am. Stat. Assoc. 110, 56–67 (2015)

    Article  MathSciNet  Google Scholar 

  14. Liu, S.T.: Quadratic programming with fuzzy parameters: a membership function approach. Chaos Solitons Fract. 40(1), 237–245 (2009)

    Article  MathSciNet  Google Scholar 

  15. Liu, S.T.: Solving quadratic programming with fuzzy parameters based on extension principle. In: FUZZ-IEEE 2007. IEEE International Conference Fuzzy Systems, pp. 1–5, London, UK (2007)

    Google Scholar 

  16. Peters, G.: Fuzzy linear regression with fuzy interval. Fuzzy Sets Syst. 63, 45–55 (1994)

    Article  Google Scholar 

  17. Redden, D.T., Woodall, W.H.: Properties of certain fuzzy linear regression methods. Fuzzy Sets Syst 64, 361–375 (1994)

    Article  MathSciNet  Google Scholar 

  18. Savic, D., Pedrycz, W.: Evaluation of fuzzy linear regression models. Fuzzy Sets Syst. 39, 51–63 (1991)

    Article  MathSciNet  Google Scholar 

  19. Silva, R.C., Cruz, C., Yamakami, A.: A Parametric Method to Solve Quadratic Programming Problems with Fuzzy Costs. IFSA (2009)

    Google Scholar 

  20. Tanaka, H., Koyama, K., Lee, H.: Interval regression analysis based on quadratic programming. IEEE 325–329 (1996)

    Google Scholar 

  21. Tanaka, H., Lee, H.: Interval regression analysis by quadratic programming approach. IEEE Trans. Fuzzy Syst. 6(4), 473–481 (1998)

    Article  Google Scholar 

  22. Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. 12(6), 903–907 (1982)

    Article  Google Scholar 

  23. Tanaka, H., Watada, J.: Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst 27, 275–289 (1988)

    Article  MathSciNet  Google Scholar 

  24. Zimmermann, H.J.: Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst. 1, 45–55 (1978)

    Article  MathSciNet  Google Scholar 

  25. Zimmermann, H.J.: Fuzzy mathematical programming. Comput. Oper. Res. 10(4), 291–298 (1983)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Estatística e Matemática Aplicada, Centro de Ciências, Universidade Federal do Ceará, Av. Mister Hull, s/n, Campus do Pici, Bloco 910, 60440-900, Fortaleza, CE, Brazil

    Ricardo Coelho

Authors
  1. Ricardo Coelho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Coelho .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    José Luis Verdegay

  2. Department of Engineering of Systems, University of La Laguna, San Cristóbal de La Laguna, Spain

    Julio Brito

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Carlos Cruz

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Coelho, R. (2022). Power Curve Estimation of Wind Farms with Imprecise Data by Fuzzy Quadratic Programming. In: Verdegay, J.L., Brito, J., Cruz, C. (eds) Computational Intelligence Methodologies Applied to Sustainable Development Goals. Studies in Computational Intelligence, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-97344-5_12

Download citation

Publish with us

Policies and ethics

Search

Navigation