http://iet.metastore.ingenta.com
1887

Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm

Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Wind speed presents a potential seasonal pattern revealed by the self-similarity in wavelet periodogram with various scales. The corresponding seasonal pattern will promote the improvement of the short-term wind speed forecasting accuracy. In this study, a novel method for short-term wind speed forecasting using wavelet transformation (WT) and AdaBoost technique is proposed to analyse the wind speeds distribution features and promote the model configuration. Power spectrum and seasonal pattern analysis using the WT are presented to investigate the wind speeds feature distribution based on the scalogram percentage of energy distribution in different seasons. This procedure contributes to perfecting the investigation of wind speed seasonal pattern characteristics over time and promotes the sample division by computing the statistics measurement based on the estimated frequencies interval. The model order estimation based on the information criteria is processed to reflect the systems dynamical sustainability between the current outputs and historical data. Finally, the experiments based on the real data from Yunnan wind farm are given to verify the effectiveness of the proposed approach.

References

    1. 1)
      • A. Costa , A. Crespo , J. Navarro .
        1. Costa, A., Crespo, A., Navarro, J., et al: ‘A review on the young history of the wind power short-term prediction’, Renew. Sustain. Energy Rev., 2008, 12, (6), pp. 17251744.
        . Renew. Sustain. Energy Rev. , 6 , 1725 - 1744
    2. 2)
      • Y. Atwa , E. El-Saadany .
        2. Atwa, Y., El-Saadany, E.: ‘Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems’, IET Renewable Power Generation, 2011, 5, (1), pp. 7988.
        . IET Renewable Power Generation , 1 , 79 - 88
    3. 3)
      • P. Srikantha , D. Kundur .
        3. Srikantha, P., Kundur, D.: ‘Distributed optimization of dispatch in sustainable generation systems via dual decomposition’, IEEE Trans. Smart Grid, 2015, 6, (5), pp. 25012509.
        . IEEE Trans. Smart Grid , 5 , 2501 - 2509
    4. 4)
      • H. MacDonald , D. Infield , D. Nash .
        4. MacDonald, H., Infield, D., Nash, D., et al: ‘Mapping hail meteorological observations for prediction of erosion in wind turbines’, Wind Energy, 2016, 19, pp. 777784.
        . Wind Energy , 777 - 784
    5. 5)
      • S. Kurian , S. Krishnan , E. Cheriyan .
        5. Kurian, S., Krishnan, S., Cheriyan, E.: ‘Real time implementation of artificial neural networks-based controller for battery storage supported wind electric generation’, IET Gener. Transm. Distrib., 2015, 9, (10), pp. 937946.
        . IET Gener. Transm. Distrib. , 10 , 937 - 946
    6. 6)
      • W. Yeh , Y. Yeh , P. Chang .
        6. Yeh, W., Yeh, Y., Chang, P., et al: ‘Forecasting wind power in the Mai Liao wind farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization’, Int. J. Electr. Power Energy Syst., 2014, 55, pp. 741748.
        . Int. J. Electr. Power Energy Syst. , 741 - 748
    7. 7)
      • H. Wei , H. Shao , X. Deng .
        7. Wei, H., Shao, H., Deng, X.: ‘Using a model structure selection technique to forecast short-term wind speed for a wind power plant in North China’, J. Energy Eng., 2015, 142, (1), p. 04015005.
        . J. Energy Eng. , 1 , 04015005
    8. 8)
      • C. Guan , P. Luh , L. Michel .
        8. Guan, C., Luh, P., Michel, L., et al: ‘Very short-term load forecasting: wavelet neural networks with data pre-filtering’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 3041.
        . IEEE Trans. Power Syst. , 1 , 30 - 41
    9. 9)
      • P. Rajamani , D. Dey , S. Chakravorti .
        9. Rajamani, P., Dey, D., Chakravorti, S.: ‘Classification of dynamic insulation failures in transformer winding during impulse test using cross-wavelet transform aided foraging algorithm’, IET Electr. Power Appl., 2010, 4, (9), pp. 715726.
        . IET Electr. Power Appl. , 9 , 715 - 726
    10. 10)
      • P. Chen , T. Pedersen , B. Bak-Jensen .
        10. Chen, P., Pedersen, T., Bak-Jensen, B., et al: ‘ARIMA-based time series model of stochastic wind power generation’, IEEE Trans. Power Syst., 2010, 25, (2), pp. 667676.
        . IEEE Trans. Power Syst. , 2 , 667 - 676
    11. 11)
      • H. Shao , X. Deng , F. Cui .
        11. Shao, H., Deng, X., Cui, F.: ‘Short-term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China’, IET Gener. Transm. Distrib., 2016, doi: 10.1049/iet-gtd.2015.0911.
        . IET Gener. Transm. Distrib.
    12. 12)
      • D. Kukolj , E. Levi .
        12. Kukolj, D., Levi, E.: ‘Identification of complex systems based on neural and Takagi-Sugeno fuzzy model’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2004, 34, (1), pp. 272282.
        . IEEE Trans. Syst. Man Cybern. B, Cybern. , 1 , 272 - 282
    13. 13)
      • A. Moghaddam , A. Seifi .
        13. Moghaddam, A., Seifi, A.: ‘Study of forecasting renewable energies in smart grids using linear predictive filters and neural networks’, IET Renew. Power Gener., 2011, 5, (6), pp. 470480.
        . IET Renew. Power Gener. , 6 , 470 - 480
    14. 14)
      • M. Hanmandlu , B. Chauhan .
        14. Hanmandlu, M., Chauhan, B.: ‘Load forecasting using hybrid models’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 2029.
        . IEEE Trans. Power Syst. , 1 , 20 - 29
    15. 15)
      • S. Soman , H. Zareipour , O. Malik .
        15. Soman, S., Zareipour, H., Malik, O., et al: ‘A review of wind power and wind speed forecasting methods with different time horizons’. IEEE Conf. on North American Power Symp. (NAPS), Arlington, USA, 26–28 September 2010, pp. 18.
        . IEEE Conf. on North American Power Symp. (NAPS) , 1 - 8
    16. 16)
      • Q. Jiang , H. Hong .
        16. Jiang, Q., Hong, H.: ‘Wavelet-based capacity configuration and coordinated control of hybrid energy storage system for smoothing out wind power fluctuations’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 13631372.
        . IEEE Trans. Power Syst. , 2 , 1363 - 1372
    17. 17)
      • F. Chellali , A. Khellaf , A. Belouchrani .
        17. Chellali, F., Khellaf, A., Belouchrani, A.: ‘Wavelet spectral analysis of the temperature and wind speed data at Adrar, Algeria’, Renew. Energy, 2010, 35, (6), pp. 12141219.
        . Renew. Energy , 6 , 1214 - 1219
    18. 18)
      • M. Misiti , Y. Misiti , G. Oppenheim .
        18. Misiti, M., Misiti, Y., Oppenheim, G., et al: ‘Matlab wavelet toolbox users’ guide. version 3’, Mathworks, 2004.
        . Mathworks
    19. 19)
      • H. Schwenk , Y. Bengio .
        19. Schwenk, H., Bengio, Y.: ‘Boosting neural networks’, Neural Comput., 2000, 12, (8), pp. 18691887.
        . Neural Comput. , 8 , 1869 - 1887
    20. 20)
      • R. Schapire .
        20. Schapire, R.: ‘The strength of weak learnability’, Mach. Learn., 1990, 5, (2), pp. 197227.
        . Mach. Learn. , 2 , 197 - 227
    21. 21)
      • L. Breiman .
        21. Breiman, L.: ‘Bagging predictors’, Mach. Learn., 1996, 24, (2), pp. 123140.
        . Mach. Learn. , 2 , 123 - 140
    22. 22)
      • R. Zhang , Z. Dong , Y. Xu .
        22. Zhang, R., Dong, Z., Xu, Y., et al: ‘Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine’, IET Gener. Transm. Distrib., 2013, 7, (4), pp. 391397.
        . IET Gener. Transm. Distrib. , 4 , 391 - 397
    23. 23)
      • P. Kankanala , S. Das , A. Pahwa .
        23. Kankanala, P., Das, S., Pahwa, A.: ‘AdaBoost: an ensemble learning approach for estimating weather-related outages in distribution systems’, IEEE Trans. Power Syst., 2014, 29, (1), pp. 359367.
        . IEEE Trans. Power Syst. , 1 , 359 - 367
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0118
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0118
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address