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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 9895639, 10 pages
http://dx.doi.org/10.1155/2016/9895639
Research Article

A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

Department of Economics and Management, North China Electric Power University, Baoding 071003, China

Received 14 April 2016; Revised 14 October 2016; Accepted 23 November 2016

Academic Editor: Marco Mussetta

Copyright © 2016 Xiping Wang and Yaqi Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. Peng, G. Fan, M. Huang, and W. Hong, “Hybridizing DEMD and quantum PSO with SVR in electric load forecasting,” Energies, vol. 9, no. 3, article no. 221, 2016. View at Publisher · View at Google Scholar
  2. G. Fan, L. Peng, W. Hong, and F. Sun, “Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression,” Neurocomputing, vol. 173, pp. 958–970, 2016. View at Publisher · View at Google Scholar
  3. J. Massana, C. Pous, L. Burgas, J. Melendez, and J. Colomer, “Short-term load forecasting in a non-residential building contrasting models and attributes,” Energy & Buildings, vol. 92, pp. 322–330, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. J.-X. Che, “A novel hybrid model for bi-objective short-term electric load forecasting,” International Journal of Electrical Power and Energy Systems, vol. 61, pp. 259–266, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Bennett, R. A. Stewart, and J. Lu, “Autoregressive with exogenous variables and neural network short-term load forecast models for residential low voltage distribution networks,” Energies, vol. 7, no. 5, pp. 2938–2960, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. A. D. Papalexopoulos and T. C. Hesterberg, “A regression-based approach to short-term system load forecasting,” IEEE Transactions on Power Systems, vol. 5, no. 4, pp. 1535–1547, 1990. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-M. Lee and C.-N. Ko, “Short-term load forecasting using lifting scheme and ARIMA models,” Expert Systems with Applications, vol. 38, no. 5, pp. 5902–5911, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. C.-M. Huang, C.-J. Huang, and M.-L. Wang, “A particle swarm optimization to identifying the ARMAX model for short-term load forecasting,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 1126–1133, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Guan, P. B. Luh, L. D. Michel, and Z. Chi, “Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 3806–3817, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Kandil, R. Wamkeue, M. Saad, and S. Georges, “An efficient approach for short term load forecasting using artificial neural networks,” International Journal of Electrical Power & Energy Systems, vol. 28, no. 8, pp. 525–530, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Che, J. Wang, and G. Wang, “An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting,” Energy, vol. 37, no. 1, pp. 657–664, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Deihimi and H. Showkati, “Application of echo state networks in short-term electric load forecasting,” Energy, vol. 39, no. 1, pp. 327–340, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. V. N. Vapnik, “The nature of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, pp. 988–999, 1995. View at Google Scholar
  14. Z. Hu, Y. Bao, and T. Xiong, “Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression,” Applied Soft Computing, vol. 25, pp. 15–25, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Selakov, D. Cvijetinović, L. Milović, S. Mellon, and D. Bekut, “Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank,” Applied Soft Computing Journal, vol. 16, pp. 80–88, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Wang, L. Li, D. Niu, and Z. Tan, “An annual load forecasting model based on support vector regression with differential evolution algorithm,” Applied Energy, vol. 94, pp. 65–70, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. E. Ceperic, V. Ceperic, and A. Baric, “A strategy for short-term load forecasting by support vector regression machines,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4356–4364, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. Q. Meng, X. Ma, and Y. Zhou, “Forecasting of coal seam gas content by using support vector regression based on particle swarm optimization,” Journal of Natural Gas Science & Engineering, vol. 21, pp. 71–78, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Kavousi-Fard, H. Samet, and F. Marzbani, “A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting,” Expert Systems with Applications, vol. 41, no. 13, pp. 6047–6056, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. W.-C. Hong, “Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model,” Energy Conversion and Management, vol. 50, no. 1, pp. 105–117, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. X. Zhang, K. K. Lai, and S.-Y. Wang, “A new approach for crude oil price analysis based on Empirical Mode Decomposition,” Energy Economics, vol. 30, no. 3, pp. 905–918, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Huang and F. G. Schmitt, “Time dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition,” Journal of Marine Systems, vol. 130, pp. 90–100, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Hu and H. L. Liang, “Adaptive multiscale entropy analysis of multivariate neural data,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 12–15, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Wei and M.-C. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research Part C: Emerging Technologies, vol. 21, no. 1, pp. 148–162, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Wang, W. Zhang, Y. Li, J. Wang, and Z. Dang, “Forecasting wind speed using empirical mode decomposition and ELMAN neural network,” Applied Soft Computing, vol. 23, pp. 452–459, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. C.-F. Chen, M.-C. Lai, and C.-C. Yeh, “Forecasting tourism demand based on empirical mode decomposition and neural network,” Knowledge-Based Systems, vol. 26, pp. 281–287, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. Z.-H. Zhu, Y.-L. Sun, and Y. Ji, “Short-term load forecasting based on EMD and SVM,” High Voltage Engineering, vol. 33, no. 5, pp. 118–122, 2007. View at Google Scholar · View at Scopus
  28. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at MathSciNet
  29. M. Hu and H. Liang, “Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis,” Cognitive Neurodynamics, vol. 5, no. 3, pp. 277–284, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. L. Yu, S. Y. Wang, and K. K. Lai, “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm,” Energy Economics, vol. 30, no. 5, pp. 2623–2635, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Liu, C. Chen, H.-Q. Tian, and Y.-F. Li, “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks,” Renewable Energy, vol. 48, pp. 545–556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Liang, S. L. Bressler, R. Desimone, and P. Fries, “Empirical mode decomposition: a method for analyzing neural data,” Neurocomputing, vol. 65-66, pp. 801–807, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. X. An, D. Jiang, M. Zhao, and C. Liu, “Short-term prediction of wind power using EMD and chaotic theory,” Communications in Nonlinear Science & Numerical Simulation, vol. 17, no. 2, pp. 1036–1042, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. C.-S. Lin, S.-H. Chiu, and T.-Y. Lin, “Empirical mode decomposition-based least squares support vector regression for foreign exchange rate forecasting,” Economic Modelling, vol. 29, no. 6, pp. 2583–2590, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, “Support vector regression machines,” Advances in Neural Information Processing Systems, vol. 28, pp. 779–784, 1996. View at Google Scholar
  36. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, Western Australia, December 1995. View at Scopus
  37. F. X. Diebold and R. S. Mariano, “Comparing predictive accuracy,” Journal of Business & Economic Statistics, vol. 13, no. 3, pp. 134–144, 1995. View at Google Scholar