About this Journal Submit a Manuscript Table of Contents
Abstract and Applied Analysis
Volume 2013 (2013), Article ID 208964, 9 pages
http://dx.doi.org/10.1155/2013/208964
Research Article

A New Strategy for Short-Term Load Forecasting

1School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China
2School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China

Received 28 February 2013; Accepted 22 April 2013

Academic Editor: Fuding Xie

Copyright © 2013 Yi Yang 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. H. M. Al-Hamadi and S. A. Soliman, “Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model,” Electric Power Systems Research, vol. 68, no. 1, pp. 47–59, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Senjyu, P. Mandal, K. Uezato, and T. Funabashi, “Next day load curve forecasting using hybrid correction method,” IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 102–109, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Wang, N. L. Tai, H. Q. Zhai, J. Ye, J. D. Zhu, and L. B. Qi, “A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting,” Electric Power Systems Research, vol. 78, no. 10, pp. 1679–1685, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. P. A. Mastorocostas, J. B. Theocharis, S. J. Kiartzis, and A. G. Bakirtzis, “A hybrid fuzzy modeling method for short-term load forecasting,” Mathematics and Computers in Simulation, vol. 51, no. 3-4, pp. 221–232, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. S. E. Papadakis, J. B. Theocharis, and A. G. Bakirtzis, “A load curve based fuzzy modeling technique for short-term load forecasting,” Fuzzy Sets and Systems, vol. 135, no. 2, pp. 279–303, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  6. J. F. Yang and J. Stenzel, “Short-term load forecasting with increment regression tree,” Electric Power Systems Research, vol. 76, no. 9-10, pp. 880–888, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. H. M. Al-Hamadi and S. A. Soliman, “Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model,” Electric Power Systems Research, vol. 68, no. 1, pp. 47–59, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Q. Kang, X. Cheng, Q. Xia, Y. H. Huang, and F. Gao, “Novel approach considering load-relative factors in short-term load forecasting,” Electric Power Systems Research, vol. 70, no. 2, pp. 99–107, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Fan, L. N. Chen, and W. J. Lee, “Machine learning based switching model for electricity load forecasting,” Energy Conversion and Management, vol. 49, no. 6, pp. 1331–1344, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Srinivasan, “Evolving artificial neural networks for short term load forecasting,” Neurocomputing, vol. 23, no. 1–3, pp. 265–276, 1998. View at Publisher · View at Google Scholar · View at Scopus
  11. J. F. Chen, W. M. Wang, and C. M. Huang, “Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting,” Electric Power Systems Research, vol. 34, no. 3, pp. 187–196, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. L. F. Amaral, R. C. Souza, and M. Stevenson, “A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting,” International Journal of Forecasting, vol. 24, no. 4, pp. 603–615, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. T. M. Choi, Y. Yu, and K. F. Au, “A hybrid SARIMA wavelet transform method for sales forecasting,” Decision Support Systems, vol. 51, no. 1, pp. 130–140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Egrioglu, C. H. Aladag, U. Yolcu, M. A. Basaran, and V. R. Uslu, “A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model,” Expert Systems with Applications, vol. 36, no. 4, pp. 7424–7434, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Y. Chen and C. H. Wang, “A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan,” Expert Systems with Applications, vol. 32, no. 1, pp. 254–264, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Liu, V. Chandrasekar, and G. Xu, “An adaptive neural network scheme for radar rainfall estimation form WSR-88D observations,” Journal of Applied Meteorology, vol. 40, no. 11, pp. 2038–2050, 2001.
  17. L. Ke, G. Wenyan, S. Xiaoliu, and T. Zhongfu, “Research on the forecast model of electricity power industry loan based on GA-BP neural network,” Energy Procedia, vol. 14, pp. 1918–1924, 2012. View at Publisher · View at Google Scholar
  18. Q. Li, J. Y. Yu, B. C. Mu, and X. D. Sun, “BP neural network prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction,” Materials Science and Engineering A, vol. 419, no. 1-2, pp. 214–217, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Li and W. Chen, “Application of BP neural network algorithm in sustainable development of highway construction projects,” Physics Procedia, vol. 25, pp. 1212–1217, 2012. View at Publisher · View at Google Scholar
  20. Y. H. Bao and J. Ren, “Wetland landscape classification based on the BP neural network in DaLinor lake area,” Procedia Environmental Sciences, vol. 10, pp. 2360–2366, 2011. View at Publisher · View at Google Scholar
  21. F. M. Tseng, H. C. Yu, and G. H. Tzeng, “Combining neural network model with seasonal time series ARIMA model,” Technological Forecasting and Social Change, vol. 69, no. 1, pp. 71–87, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. S. L. Lai and W. L. Lu, “Impact analysis of September 11 on air travel demand in the USA,” Journal of Air Transport Management, vol. 11, no. 6, pp. 455–458, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. F. M. Tseng and G. H. Tzeng, “A fuzzy seasonal ARIMA model for forecasting,” Fuzzy Sets and Systems, vol. 126, no. 3, pp. 367–376, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  24. M. A. Mohandes, S. Rehman, and T. O. Halawani, “A neural networks approach for wind speed prediction,” Renewable Energy, vol. 13, no. 3, pp. 345–354, 1998. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Yu, S. 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
  26. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Publisher · View at Google Scholar · View at Scopus
  27. H. S. Hippert, C. E. Pedreira, and R. C. Souza, “Neural networks for short-term load forecasting: a review and evaluation,” IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44–55, 2001. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. D. Zhang and L. N. Wu, “Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network,” Expert Systems with Applications, vol. 36, no. 5, pp. 8849–8854, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. C. X. J. Feng, C. G. Abhirami, A. E. Smith, and Z. G. S. Yu, “Practical guidelines for developing BP neural network models of measurement uncertainty data,” Journal of Manufacturing Systems, vol. 25, no. 4, pp. 239–250, 2006. View at Publisher · View at Google Scholar · View at Scopus