Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
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Short-Term Load Forecasting Based on Sequential Relevance Vector Machine

  • Jang, Youngchan (Weapon System Engineering Department, Korea Army Academy at Yeongcheon)
  • Received : 2015.05.13
  • Accepted : 2015.09.14
  • Published : 2015.09.30
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Abstract

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

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References

  1. Al-Hamadi, H. M. and Soliman, S. A. (2004), Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model, Electric, Power Syst. Res., 68(1), 47-59. https://doi.org/10.1016/S0378-7796(03)00150-0
  2. Amjady, N. and Keynia, S. (2009), Short term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm, Energy, 34, 46-57. https://doi.org/10.1016/j.energy.2008.09.020
  3. Bashir, Z. A. and El-Hawary, M. E. (2009), Applying wavelets to short-term load forecasting using PSObased neural networks, IEEE Trans. Power Syst., 24, 20-27.
  4. Benaouda, D., Murtagh, F., Starck, J. L. and Renaud, O. (2006), Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting, Neurocomputing, 70, 139-154. https://doi.org/10.1016/j.neucom.2006.04.005
  5. Che, J., Wang, J., and Wang G. (2012), An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting, Energy, 37(1), 657-664. https://doi.org/10.1016/j.energy.2011.10.034
  6. Dongxiao, N., Wang, Y., and Wu, D. D. (2009), Power load forecasting using support vector machine and ant colony optimization, Expert Systems with Applications, 37(3), 2531-2539. https://doi.org/10.1016/j.eswa.2009.08.019
  7. Fan, S. and Chen, L. (2006), Short-term load forecasting based on an adaptive hybrid method, IEEE Trans. Power Syst., 21(1), 392-401. https://doi.org/10.1109/TPWRS.2005.860944
  8. Hagan, M. T. and Behr, S. M. (1987), The time series approach to short term load forecasting, IEEE Trans. Power Syst., 2(3), 785-791. https://doi.org/10.1109/TPWRS.1987.4335210
  9. Hippert, H. S., Pedreira, C. E., and Souza, R. C. (2001), Neural networks for short-term load forecasting: A review and evaluation, IEEE Trans. Power Syst., 16(1), 44-55. https://doi.org/10.1109/59.910780
  10. Hsu, C. W., Chang, C. C., and Lin, C. J. (2003), A practical guide to support vector classification, Department of Computer Science, National Taiwan University.
  11. Nikolaev, N. and Tino, P. (2005), Sequential relevance vector machine learning from time series, Neural Networks, IJCNN, Proceedings, IEEE International Joint Conference on, 2, 1308-1313
  12. Pandey, A. S., Singh, D., and Sinha, S. K. (2010), Intelligent hybrid wavelet models for short-term load forecasting, IEEE Trans. Power Syst., 25, 1266-1273. https://doi.org/10.1109/TPWRS.2010.2042471
  13. Ping-Feng, P. and Wei-Chiang, H. (2005), Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Conversion and Management, 46(17), 2669-2688. https://doi.org/10.1016/j.enconman.2005.02.004
  14. Qing Duan, J.-G., Zhao, L. N., and Ke, L. (2008), Regression Based on Sparse Bayesian Learning and the Applications in Electric Systems, Natural Computation, Fourth International Conference on, 106-110.
  15. Rocha Reis, J. and da Silva, A. P. A. (2005), Feature extraction via multiresolution analysis for shortterm load forecasting, IEEE Trans. Power Syst., 20, 189-198. https://doi.org/10.1109/TPWRS.2004.840380
  16. Song, K. B., Baek, Y. S., Hong, D. H., and Jang, G. (2005), Short-term load forecasting for the holidays using fuzzy linear regression method, IEEE Trans. Power Syst., 20(1), 96-101. https://doi.org/10.1109/TPWRS.2004.835632
  17. Taylor, J. W. and McSharry, P. E. (2007), Short-Term Load Forecasting Methods: An Evaluation Based on European Data, IEEE Trans. Power Syst., 22(4), 2213-2219. https://doi.org/10.1109/TPWRS.2007.907583
  18. Tipping, M. E. (2001), Sparse Bayesian learning and the relevance vector machine, J. Mach. Learn. Res., 1, 211-244.
  19. Zhang, B.-L. and Dong, Z.-Y. (2001), An adaptive neural-wavelet model for short term load forecasting, Electr. Power Syst. Res., 59, 121-129. https://doi.org/10.1016/S0378-7796(01)00138-9
  20. Zhinong, W., Xiaolu, L., Cheung, K. W., Jiang, W., and Shuaidong, H. (2013), A new short-term load forecasting model based on relevance vector machine, Electricity Distribution, 22nd International Conference and Exhibition on, 10-13.