Advertisement

Climate Dynamics

, Volume 46, Issue 5–6, pp 1893–1907 | Cite as

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

  • Shahaboddin Shamshirband
  • Kasra Mohammadi
  • Chong Wen Tong
  • Dalibor Petković
  • Emilio Porcu
  • Ali Mostafaeipour
  • Sudheer Ch
  • Ahmad Sedaghat
Article

Abstract

The knowledge of the probabilistic wind speed distribution is of particular significance in reliable evaluation of the wind energy potential and effective adoption of site specific wind turbines. Among all proposed probability density functions, the two-parameter Weibull function has been extensively endorsed and utilized to model wind speeds and express wind speed distribution in various locations. In this research work, extreme learning machine (ELM) is employed to compute the shape (k) and scale (c) factors of Weibull distribution function. The developed ELM model is trained and tested based upon two widely successful methods used to estimate k and c parameters. The efficiency and accuracy of ELM is compared against support vector machine, artificial neural network and genetic programming for estimating the same Weibull parameters. The survey results reveal that applying ELM approach is eventuated in attaining further precision for estimation of both Weibull parameters compared to other methods evaluated. Mean absolute percentage error, mean absolute bias error and root mean square error for k are 8.4600 %, 0.1783 and 0.2371, while for c are 0.2143 %, 0.0118 and 0.0192 m/s, respectively. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate Weibull k and c factors.

Keywords

Wind speed distribution Weibull function Extreme learning machine (ELM) Shape factor Scale factor 

Notes

Acknowledgments

The authors would like to thank the University of Malaya for the research grants allocated (UMRG RP015C-13AET and High Impact Research Grant, HIR-D000006-16001). Special appreciation is also credited to the Malaysian Ministry of Education, MOE for the Fundamental Research Grant Scheme (FP053-2013B). The authors would like to thank the Bright Spark Unit of University of Malaya for the financial support.

References

  1. Akdag SA, Dinler A (2009) A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage 50:1761–1766CrossRefGoogle Scholar
  2. Andrade CFd, Neto HFM, Costa Rocha PA, da Silva MEV (2014) An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil. Energy Convers Manag 86:801–808CrossRefGoogle Scholar
  3. Annema AJ, Hoen K, Wallinga H (1994) Precision requirements for single-layer feedforward neural networks, In: fourth international conference on microelectronics for neural networks and fuzzy systems, p. 145‒51Google Scholar
  4. Arslan T, Bulut YM, Yavuz AA (2014) Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renew Sustain Energy Rev 40:820–825CrossRefGoogle Scholar
  5. Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318(1):7–16CrossRefGoogle Scholar
  6. Aziz A, Wong K (1992) Neural-network approach to the determination of aquifer parameters. Ground Water GRWAAP 30:164–166CrossRefGoogle Scholar
  7. Babovic V, Keijzer M (2000) Rainfall runoff modeling based on genetic programming. Nord Hydrol 33:331–346CrossRefGoogle Scholar
  8. Balkhair K (2002) Aquifer parameters determination for large diameter wells using neural network approach. J Hydrol 265:118–128CrossRefGoogle Scholar
  9. Breton SP, Moe G (2009) Status, plans and technologies for offshore wind turbines in Europe and North America. Renew Energy 34(3):646–654CrossRefGoogle Scholar
  10. Chau K (2007) Reliability and performance-based design by artificial neural network. Adv Eng Softw 38:145–149CrossRefGoogle Scholar
  11. Chellali F, Khellaf A, Belouchrani A, Khanniche R (2012) A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria. Renew Sustain Energy Rev 16:379–385CrossRefGoogle Scholar
  12. Collobert R, Bengio S (2000) Support vector machines for large-scale regression problems. Institut Dalle Molle d’Intelligence Artificelle Perceptive (IDIAP), Martigny, Switzerland, Technical Report IDIAP-RR-00-17Google Scholar
  13. Curry CL, Dvd Kamp, Monahan AH (2012) Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed. Clim Dyn 38:1281–1299CrossRefGoogle Scholar
  14. Demuth H, Beale M (1997) Neural network toolbox for use with MATLAB, users guide, version 3.0. The Mathworks Inc., Natick., MassGoogle Scholar
  15. García-Bustamante E, González-Rouco JF, Navarro J, Xoplaki E, Luterbacher J, Jiménez PA et al (2013) Relationship between wind power production and North Atlantic atmospheric circulation over the northeastern Iberian Peninsula. Clim Dyn 40:935–949CrossRefGoogle Scholar
  16. Ghouti L, Sheltami TR, Alutaibi KS (2013) Mobility prediction in mobile ad hoc networks using extreme learning machines. Proced Comput Sci 19:305–312CrossRefGoogle Scholar
  17. Huang C, Davis L, Townshend J (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749CrossRefGoogle Scholar
  18. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: International joint conference on neural networks, vol 2, pp 985‒990Google Scholar
  19. Mohammadi K, Mostafaeipour A, Sabzpooshani M (2014) Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran. Energy 67:117–128CrossRefGoogle Scholar
  20. Mostafaeipour A, Jadidi M, Mohammadi K, Sedaghat A (2014) An analysis of wind energy potential and economic evaluation in Zahedan, Iran. Renew Sustain Energy Rev 30:641–50CrossRefGoogle Scholar
  21. Huang GB, Zhu QY, Siew CK (2006a) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  22. Huang GB, Chen L, Siew CK (2006b) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892CrossRefGoogle Scholar
  23. Ji Y, Sun S (2013) Multitask multiclass support vector machines: model and experiments. Pattern Recogn 46(3):914–924CrossRefGoogle Scholar
  24. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Springer, New YorkGoogle Scholar
  25. Khu ST, Liong S-Y, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forecasting. J Am Water Resour Assoc 37:439–451CrossRefGoogle Scholar
  26. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT press, CambridgeGoogle Scholar
  27. Li Z, Boyle F, Reynolds A (2012) Domestic application of micro wind turbines in Ireland: investigation of their economic viability. Renew Energy 41:64–74CrossRefGoogle Scholar
  28. Liang NY, Huang GB, Rong HJ, Saratchandran P, Sundararajan N (2006) A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423CrossRefGoogle Scholar
  29. Lu W-Z, Wang W-J (2005) Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere 59(5):693–701CrossRefGoogle Scholar
  30. Manwell JF, McGowan JG, Rogers AL (2002) Wind energy explained: theory, design and application. Wiley, AmherstCrossRefGoogle Scholar
  31. Mohammadi K, Mostafaeipour A (2013a) Using different methods for comprehensive study of wind turbine utilization in Zarrineh. Iran Energy Convers Manag 65:463–470CrossRefGoogle Scholar
  32. Mohammadi K, Mostafaeipour A (2013b) Economic feasibility of developing wind turbines in Aligoodarz. Iran Energy Convers Manag 76:645–653CrossRefGoogle Scholar
  33. Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. IJCNN ‘02. IEEE proceedings of the 2002 international joint conference on neural networks, pp. 1702‒7Google Scholar
  34. Nian R, He B, Zheng B, Heeswijk MV, Yu Q, Miche Y et al (2014) Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing 128:273–284CrossRefGoogle Scholar
  35. O’Rourke F, Boyle F, Reynolds A (2009) Renewable energy resources and technologies applicable to Ireland. Renew Sustain Energy Rev 13(8):1975–1984CrossRefGoogle Scholar
  36. Ouammi A, Dagdougui H, Sacile R, Mimet A (2010) Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy). Renew Sustain Energy Rev 14:1959–1968CrossRefGoogle Scholar
  37. Petković D, Shamshirband S, Anuar NB, Saboohi H, Abdul Wahab AW, Protić M et al (2014) An appraisal of wind speed distribution prediction by soft computing methodologies: a comparative study. Energy Convers Manag 84:133–139CrossRefGoogle Scholar
  38. Pishgar-Komleh SH, Keyhani A, Sefeedpari P (2015) Wind speed and power density analysis based on Waybill and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renew Sustain Energy Rev 42:313–322CrossRefGoogle Scholar
  39. Pryor SC, Barthelmie RJ, Kjellstrӧm E (2005) Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model. Clim Dyn 25:815–835CrossRefGoogle Scholar
  40. Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35(16):1578–1587CrossRefGoogle Scholar
  41. Sahu BK, Hiloidhari M, Baruah DC (2013) Global trend in wind power with special focus on the top five wind power producing countries. Renew Sustain Energy Rev 19:348–359CrossRefGoogle Scholar
  42. Salcedo-Sanz S, Pastor-Sánchez A, Prieto L, Blanco-Aguilera A, García-Herrera R (2014) Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization–extreme learning machine approach. Energy Convers Manag 87:10–18CrossRefGoogle Scholar
  43. Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill Higher Education, NewYorkGoogle Scholar
  44. Shamshirband S, Petković D, Saboohi H, Anuar NB, Inayat I, Akib S et al (2014) Wind turbine power coefficient estimation by soft computing methodologies: comparative study. Energy Convers Manag 81:520–526CrossRefGoogle Scholar
  45. Singh R, Balasundaram S (2007) Application of extreme learning machine method for time series analysis. Int J Intell Technol 2:256–262Google Scholar
  46. Sudheer C, Mathur S (2012) Particle swarm optimization trained neural network for aquifer parameter estimation KSCE. J Civil Eng 16:298–307Google Scholar
  47. Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038CrossRefGoogle Scholar
  48. Sung AH, Mukkamala S (2003) Identifying important features for intrusion detection using support vector machines and neural networks. In: applications and the internet, proceedings, 2003 symposium on IEEE 209‒16Google Scholar
  49. Ucar A, Balo F (2010) Assessment of wind power potential for turbine installation in coastal areas of Turkey. Renew Sustain Energy Rev 14:1901–1912CrossRefGoogle Scholar
  50. Vapnik V (2000) The nature of statistical learning theory. Springer, BerlinCrossRefGoogle Scholar
  51. Vapnik VN, Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  52. Vapnik V, Golowich SE, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, pp 281‒87Google Scholar
  53. Wan C, Xu Z, Pinson P, Yang Dong Z, Po Wong K (2014) Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans Power Syst 29:1033–1044CrossRefGoogle Scholar
  54. Wang X, Han M (2014) Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 145:90–97CrossRefGoogle Scholar
  55. Wang DD, Wang R, Yan H (2014) Fast prediction of protein–protein interaction sites based on extreme learning machines. Neurocomputing 128:258–266CrossRefGoogle Scholar
  56. Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energy 74:640–647CrossRefGoogle Scholar
  57. Wu K-P, Wang S-D (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42(5):710–717CrossRefGoogle Scholar
  58. Wu S, Wang Y, Cheng S (2013) Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system. Neurocomputing 102:163–175CrossRefGoogle Scholar
  59. Yang H, Huang K, King I, Lyu MR (2009) Localized support vector regression for time series prediction. Neurocomputing 72(10):2659–2669CrossRefGoogle Scholar
  60. Yu Q, Miche Y, Séverin E, Lendasse A (2014) Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing 128:296–302CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Shahaboddin Shamshirband
    • 1
  • Kasra Mohammadi
    • 2
  • Chong Wen Tong
    • 3
  • Dalibor Petković
    • 3
  • Emilio Porcu
    • 4
  • Ali Mostafaeipour
    • 5
  • Sudheer Ch
    • 6
  • Ahmad Sedaghat
    • 7
  1. 1.Department of Computer System and Technology, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala lumpurMalaysia
  2. 2.Faculty of Mechanical EngineeringUniversity of KashanKashanIran
  3. 3.Department of Mechanical Engineering, Faculty of Engineering University of MalayaKuala LumpurMalaysia
  4. 4.Technical University Federico Santa MariaValparaísoChile
  5. 5.Industrial Engineering DepartmentYazd UniversityYazdIran
  6. 6.Department of Civil and Environmental EngineeringITM UniversityGurugaonIndia
  7. 7.Department of Mechanical Engineering, School of EngineeringAustralian College of KuwaitSafatKuwait

Personalised recommendations