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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 231765, 8 pages
http://dx.doi.org/10.1155/2015/231765
Research Article

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

School of Economics and Management, North China Electric Power University, Beijing 102206, China

Received 21 October 2014; Revised 9 December 2014; Accepted 16 December 2014

Academic Editor: Dan Simon

Copyright © 2015 Dongxiao Niu et al. 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. D. X. Niu, S. H. Cao, and J. C. H. Lu, Power Load Forecasting Technique and Its Application, China Electric Power Press, 2009.
  2. C. H. Q. Kang, Q. Xia, and M. Liu, Power System Load Forecasting, China Electric Power Press, 2007.
  3. T. Hong and P. Wang, “Fuzzy interaction regression for short term load forecasting,” Fuzzy Optimization and Decision Making, vol. 13, no. 1, pp. 91–103, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. N. Amjady, “Short-term hourly load forecasting using time-series modeling with peak load estimation capability,” IEEE Transactions on Power Systems, vol. 16, no. 3, pp. 498–505, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Hernández, C. Baladrón, J. M. Aguiar et al., “Artificial neural networks for short-term load forecasting in microgrids environment,” Energy, vol. 75, pp. 252–264, 2014. View at Publisher · View at Google Scholar
  6. M. Y. Chow and H. Tram, “Application of fuzzy logic technology for spatial load forecasting,” IEEE Transactions on Power Systems, vol. 12, no. 3, pp. 1360–1366, 1997. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Srinivasan and S. S. Tan, “Parallel neural network-fuzzy expert system strategy for short-term load forecasting: system implementation and performance evaluation,” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1100–1106, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. W. J. Staszewski and K. Worden, “Wavelet analysis of time-series: coherent structures, chaos and noise,” International Journal of Bifurcation and Chaos, vol. 9, no. 3, pp. 455–471, 1999. View at Google Scholar · View at Scopus
  9. H. Mori and S. Vrano, “Short-term load forecasting with chaos time series analysis,” in Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, pp. 283–287, 2007. View at Scopus
  10. J. X. Che and J. Z. Wang, “Short-term load forecasting using a kernel-based support vector regression combination model,” Applied Energy, vol. 132, pp. 602–609, 2014. View at Publisher · View at Google Scholar
  11. A. Deihimi, O. Orang, and H. Showkati, “Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction,” Energy, vol. 57, pp. 382–401, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search,” International Journal of Electrical Power and Energy Systems, vol. 62, pp. 118–129, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. H. Xiao and S. J. Ye, “Rough set method for short-term load forecasting,” Journal of Systems Engineering, vol. 24, no. 2, pp. 143–149, 2009. View at Google Scholar
  14. C. H. Kim, B. G. Koo, and J. H. Park, “Short-term electric load forecasting using data mining technique,” Journal of Electrical Engineering and Technology, vol. 7, no. 6, pp. 807–813, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Z. H. Zhu, “Short-term electric load forecasting with combined data mining algorithm,” Automation of Electric Power Systems, vol. 30, no. 14, pp. 82–86, 2006. View at Google Scholar · View at Scopus
  16. D. X. Niu and Y. N. Wei, “Short-term power load combinatorial forecast adaptively weighted by FHNN similar-day clustering,” Automation of Electric Power Systems, vol. 37, no. 3, pp. 54–57, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. 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
  18. M. Yoder, A. S. Hering, W. C. Navidi, and K. Larson, “Short-term forecasting of categorical changes in wind power with Markov chain models,” Wind Energy, vol. 17, no. 9, pp. 1425–1439, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Bahrami, R.-A. Hooshmand, and M. Parastegari, “Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm,” Energy, vol. 72, pp. 434–442, 2014. View at Publisher · View at Google Scholar
  20. S. H. D. Pan, Z. H. N. Wei, Z. H. Gao et al., “A short-term load forecasting model based on relevance vector machine with nonnegative matrix factorization,” Automation of Electric Power Systems, vol. 36, no. 11, pp. 62–66, 2012. View at Google Scholar
  21. Q.-H. Zhang, “A model for short-term load forecasting in power system based on multi-AI methods,” System Engineering Theory and Practice, vol. 33, no. 2, pp. 354–362, 2013. View at Google Scholar · View at Scopus
  22. Z. G. Qian and X. Z. Lin, “Detection algorithm of image corner based on contour sharp degree,” Computer Engineering, vol. 34, no. 6, pp. 202–204, 2008. View at Google Scholar
  23. B. Amrouche and X. Le Pivert, “Artificial neural network based daily local forecasting for global solar radiation,” Applied Energy, vol. 130, pp. 333–341, 2014. View at Publisher · View at Google Scholar
  24. R. Mohammadi, S. M. T. F. Ghomi, and F. Zeinali, “A new hybrid evolutionary based RBF networks method for forecasting time series: a case study of forecasting emergency supply demand time series,” Engineering Applications of Artificial Intelligence, vol. 36, pp. 204–214, 2014. View at Publisher · View at Google Scholar
  25. H. Y. Luo, T. Q. Liu, and X. Y. Li, “Chaotic forecasting method of short-term wind speed in wind farm,” Power System Technology, vol. 33, no. 9, pp. 67–71, 2009. View at Google Scholar
  26. M. D. Alfaro, J. M. Sepúlveda, and J. A. Ulloa, “Forecasting chaotic series in manufacturing systems by vector support machine regression and neural networks,” International Journal of Computers Communications & Control, vol. 8, no. 1, pp. 8–17, 2013. View at Publisher · View at Google Scholar
  27. Z. Y. Zhang, T. Wang, and X. G. Liu, “Melt index prediction by aggregated RBF neural networks trained with chaotic theory,” Neurocomputing, vol. 131, pp. 368–376, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. H. B. Bi and Y. B. Zhang, “Application of the chaotic RBF neural network on electrical loads prediction,” Science, Technology and Engineering, vol. 9, no. 24, pp. 7480–7492, 2009. View at Google Scholar
  29. W. Y. Zhang, W. C. Hong, Y. Dong, G. Tsai, J. T. Sung, and G. F. Fan, “Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting,” Energy, vol. 45, no. 1, pp. 850–858, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Kouhi, F. Keynia, and S. N. Ravadanegh, “A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection,” International Journal of Electrical Power & Energy Systems, vol. 62, pp. 862–867, 2014. View at Publisher · View at Google Scholar
  31. X.-Q. Lu, B. Cao, M. Zeng, S.-S. Huang, and X.-G. Liu, “Algorithm of selecting delay time in the mutual information method,” Chinese Journal of Computational Physics, vol. 23, no. 2, pp. 184–188, 2006. View at Google Scholar · View at Scopus
  32. K. Aihara, T. Takabe, and M. Toyoda, “Chaotic neural networks,” Physics Letters A, vol. 144, no. 6-7, pp. 333–340, 1990. View at Publisher · View at Google Scholar · View at MathSciNet
  33. Y. R. Cheng and S. B. Guo, “Stock price prediction based on analysis of chaotic time series,” Journal of UEST of China, vol. 32, no. 4, pp. 469–472, 2003. View at Google Scholar
  34. S.-Q. Zhang, J. Jia, M. Gao, and X. Han, “Study on the parameters determination for reconstructing phase-space in chaos time series,” Acta Physica Sinica, vol. 59, no. 3, pp. 1576–1582, 2010. View at Google Scholar · View at Scopus
  35. D. Kressner, M. Ple{\vS}inger, and C. Tobler, “A preconditioned low-rank CG method for parameter-dependent Lyapunov matrix equations,” Numerical Linear Algebra with Applications, vol. 21, no. 5, pp. 666–684, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. J. Z. Zhou, Y. C. Zhang, Q. Q. Li, and J. Guo, “Probabilistic short-term load forecasting based on dynamic self-adaptive radial basis function network,” Power System Technology, vol. 34, no. 3, pp. 37–41, 2010. View at Google Scholar · View at Scopus
  37. H. L. Deng and X. Q. Li, “Stock price inflection point prediction method Based on chaotic time series analysis,” Statistics and Decision, no. 5, pp. 19–20, 2007. View at Google Scholar