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Mathematical Problems in Engineering
Volume 2016, Article ID 3205396, 28 pages
http://dx.doi.org/10.1155/2016/3205396
Research Article

A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

1School of Statistics, Dongbei University of Finance and Economics, Dalian, Liaoning 116025, China
2School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu 730000, China

Received 20 August 2015; Revised 8 April 2016; Accepted 13 April 2016

Academic Editor: Dongsuk Kum

Copyright © 2016 Jiani Heng 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. S. Kouhi, F. Keynia, and S. Najafi Ravadanegh, “A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection,” International Journal of Electrical Power and Energy Systems, vol. 62, pp. 862–867, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. 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
  3. Y. Zhang and G. Luo, “Short term power load prediction with knowledge transfer,” Information Systems, vol. 53, pp. 161–169, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. N. An, W. Zhao, J. Wang, D. Shang, and E. Zhao, “Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting,” Energy, vol. 49, no. 1, pp. 279–288, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. I. Moghram and S. Rahman, “Analysis and evaluation of five short-term load forecasting techniques,” IEEE Transactions on Power Systems, vol. 4, no. 4, pp. 1484–1491, 1989. View at Publisher · View at Google Scholar · View at Scopus
  6. M. T. Hagan and S. M. Behr, “The time series approach to short term load forecasting,” IEEE Transactions on Power Systems, vol. 2, no. 3, pp. 785–791, 1987. View at Google Scholar · View at Scopus
  7. P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. V. Dordonnat, S. J. Koopman, M. Ooms, A. Dessertaine, and J. Collet, “An hourly periodic state space model for modelling French national electricity load,” International Journal of Forecasting, vol. 24, no. 4, pp. 566–587, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. W. R. Christiaanse, “Short-term load forecasting using general exponential smoothing,” IEEE Transactions on Power Apparatus and Systems, vol. 90, no. 2, pp. 900–911, 1971. View at Google Scholar · View at Scopus
  10. W.-C. Hong, “Hybrid evolutionary algorithms in a SVR-based electric load forecasting model,” International Journal of Electrical Power & Energy Systems, vol. 31, no. 7-8, pp. 409–417, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. V. H. Hinojosa and A. Hoese, “Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 565–574, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. P. K. Dash, A. C. Liew, S. Rahman, and G. Ramakrishna, “Building a fuzzy expert system for electric load forecasting using a hybrid neural network,” Expert Systems with Applications, vol. 9, no. 3, pp. 407–421, 1995. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Rahman and O. Hazim, “Load forecasting for multiple sites: development of an expert system-based technique,” Electric Power Systems Research, vol. 39, no. 3, pp. 161–169, 1996. View at Publisher · View at Google Scholar · View at Scopus
  14. D. K. Chaturvedi, A. P. Sinha, and O. P. Malik, “Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network,” International Journal of Electrical Power and Energy Systems, vol. 67, pp. 230–237, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Kouhi and F. Keynia, “A new cascade NN based method to short-term load forecast in deregulated electricity market,” Energy Conversion and Management, vol. 71, pp. 76–83, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Li, Y. Li, Q. Xiong, Y. Chai, and Y. Zhang, “Application of a hybrid quantized Elman neural network in short-term load forecasting,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 749–759, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. 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
  18. R. Mamlook, O. Badran, and E. Abdulhadi, “A fuzzy inference model for short-term load forecasting,” Energy Policy, vol. 37, no. 4, pp. 1239–1248, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Hanmandlu and B. K. Chauhan, “Load forecasting using hybrid models,” IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 20–29, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Q. Raza and Z. Baharudin, “A review on short term load forecasting using hybrid neural network techniques,” in Proceedings of the International Conference on Power and Energy (PECon '12), pp. 846–851, IEEE, Kota Kinabalu, Malaysia, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. M. El-Telbany and F. El-Karmi, “Short-term forecasting of Jordanian electricity demand using particle swarm optimization,” Electric Power Systems Research, vol. 78, no. 3, pp. 425–433, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. A. B. Nutt, R. C. Lenz Jr., H. W. Lanford, and M. J. Cleary, “Data sources for trend extrapolation in technological forecasting,” Long Range Planning, vol. 9, no. 1, pp. 72–76, 1976. View at Publisher · View at Google Scholar · View at Scopus
  23. M. M. Tripathi, K. G. Upadhyay, and S. N. Singh, “Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market,” The Electricity Journal, vol. 21, no. 9, pp. 24–34, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. 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
  25. N. Ding, Y. Bésanger, and F. Wurtz, “Next-day MV/LV substation load forecaster using time series method,” Electric Power Systems Research, vol. 119, pp. 345–354, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. O. Valenzuela, I. Rojas, F. Rojas et al., “Hybridization of intelligent techniques and ARIMA models for time series prediction,” Fuzzy Sets and Systems, vol. 159, no. 7, pp. 821–845, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  27. H. Nie, G. Liu, X. Liu, and Y. Wang, “Hybrid of ARIMA and SVMs for short-term load forecasting,” Energy Procedia, vol. 16, pp. 1455–1460, 2012. View at Publisher · View at Google Scholar
  28. T. Yalcinoz and U. Eminoglu, “Short term and medium term power distribution load forecasting by neural networks,” Energy Conversion and Management, vol. 46, no. 9-10, pp. 1393–1405, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. C. J. Bennett, R. A. Stewart, and J. W. Lu, “Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system,” Energy, vol. 67, pp. 200–212, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. H. S. Chen and W. C. Chang, “A study of optimal grey model GM (1, 1),” Journal of the Chinese Grey System Association, vol. 1, no. 2, pp. 141–145, 1998. View at Google Scholar
  31. L. Xiao, J. Wang, X. Yang, and L. Xiao, “A hybrid model based on data preprocessing for electrical power forecasting,” International Journal of Electrical Power and Energy Systems, vol. 64, pp. 311–327, 2015. View at Publisher · View at Google Scholar · View at Scopus
  32. 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
  33. N. Liu, Q. Tang, J. Zhang, W. Fan, and J. Liu, “A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids,” Applied Energy, vol. 129, pp. 336–345, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. 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 · View at Scopus
  35. L. Karthikeyan and D. N. Kumar, “Predictability of nonstationary time series using wavelet and EMD based ARMA models,” Journal of Hydrology, vol. 502, pp. 103–119, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. G. K. Sharma, A. Kumar, T. Jayakumar, B. P. Rao, and N. Mariyappa, “Ensemble Empirical Mode Decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels,” Ultrasonics, vol. 57, pp. 167–178, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Baliyan, K. Gaurav, and S. K. Mishra, “A review of short term load forecasting using artificial neural network models,” Procedia Computer Science, vol. 48, pp. 121–125, 2015. View at Google Scholar
  38. R. Chao, [Ph.D. thesis], Lanzhou University, 2013.
  39. N. B. Andersen, “Real Paley-Wiener theorems and Roe's theorem associated with the Opdam-Cherednik transform,” Journal of Mathematical Analysis and Applications, vol. 427, no. 1, pp. 47–59, 2015. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  40. K. Liu, W. Y. Guo, X. L. Shen, and Z. F. Tan, “Research on the forecast model of electricity power industry loan based on GA-BP neural network,” Energy Procedia, vol. 4, pp. 1918–1924, 2012. View at Google Scholar
  41. Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Transactions on Neural Networks, vol. 3, no. 6, pp. 889–898, 1992. View at Publisher · View at Google Scholar · View at Scopus
  42. Y. Lu, N. Zeng, Y. Liu, and N. Zhang, “A hybrid wavelet neural network and switching particle swarm optimization algorithm for face direction recognition,” Neurocomputing, vol. 155, pp. 219–224, 2015. View at Publisher · View at Google Scholar · View at Scopus
  43. N. M. Nawi, A. Khan, and M. Z. Rehman, “A new back-propagation neural network optimized with cuckoo search algorithm,” in Computational Science and Its Applications—ICCSA 2013: 13th International Conference, Ho Chi Minh City, Vietnam, June 24–27, 2013, Proceedings, Part I, vol. 7971 of Lecture Notes in Computer Science, pp. 413–426, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  44. A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,” Engineering with Computers, vol. 29, no. 1, pp. 17–35, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. F. Yu and X. Xu, “A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network,” Applied Energy, vol. 134, pp. 102–113, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. K. Liu, W. Guo, X. Shen, and Z. Tan, “Research on the gforecast model of electricity power industry loan based on GA-BP neural network,” Energy Procedia, vol. 14, pp. 1918–1924, 2012. View at Google Scholar
  47. M. Smith, “Modeling and short-term forecasting of new south wales electricity system load,” Journal of Business and Economic Statistics, vol. 18, no. 4, pp. 465–478, 2000. View at Google Scholar · View at Scopus
  48. S. Qin, F. Liu, J. Wang, and B. Sun, “Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models,” Atmospheric Environment, vol. 98, pp. 665–675, 2014. View at Publisher · View at Google Scholar · View at Scopus
  49. D. Bunn and E. D. Farmer, Comparative Models for Electrical Load Forecasting, John Wiley & Sons, Chichester, UK, 1985.
  50. L. Liu, Q. Wang, J. Wang, and M. Liu, “A rolling grey model optimized by particle swarm optimization in economic prediction,” Computational Intelligence, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. H. Y. Chen and D. P. Hou, “Research on superior combination forecasting models based on forecasting effective measure,” Journal of University of Science and Technology of China, vol. 32, no. 2, pp. 172–180, 2002. View at Google Scholar · View at MathSciNet