Table of Contents Author Guidelines Submit a Manuscript
Abstract and Applied Analysis
Volume 2014, Article ID 693205, 14 pages
http://dx.doi.org/10.1155/2014/693205
Research Article

Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm

Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, China

Received 9 January 2014; Accepted 13 February 2014; Published 3 April 2014

Academic Editor: Suohai Fan

Copyright © 2014 Wenyu Zhang 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. “IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation,” 2011, http://www.ipcc-wg3.de/publications/special-reports.
  2. K. Y. Oh, J. Y. Kim, J. K. Lee, M. S. Ryu, and J. S. Lee, “An assessment of wind energy potential at the demonstration offshore wind farm in Korea,” Energy, vol. 46, pp. 555–563, 2012. View at Publisher · View at Google Scholar
  3. N. Chaudhry and L. Hughes, “Forecasting the reliability of wind-energy systems: a new approach using the RL technique,” Applied Energy, vol. 96, pp. 422–430, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. GWEC, “Global Wind Energy Council,” 2010, http://www.gwec.net/fileadmin/documents/Publications/GWEO%202010%20final.pdf.
  5. W. Zhang, J. Wu, J. Wang, W. Zhao, and L. Shen, “Performance analysis of four modified approaches for wind speed forecasting,” Applied Energy, vol. 99, pp. 324–333, 2012. View at Publisher · View at Google Scholar
  6. M. Carolin Mabel and E. Fernandez, “Analysis of wind power generation and prediction using ANN: a case study,” Renewable Energy, vol. 33, no. 5, pp. 986–992, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Rehman, A. M. Mahbub Alam, J. P. Meyer, and L. M. Al-Hadhrami, “Wind speed characteristics and resource assessment using weibull parameters,” International Journal of Green Energy, vol. 9, no. 8, pp. 800–814, 2012. View at Publisher · View at Google Scholar
  8. P. Kou, F. Gao, and X. Guan, “Sparse online warped Gaussian process for wind power probabilistic forecasting,” Applied Energy, vol. 108, pp. 410–428, 2013. View at Publisher · View at Google Scholar
  9. A. Öztopal, “Artificial neural network approach to spatial estimation of wind velocity data,” Energy Conversion and Management, vol. 47, no. 4, pp. 395–406, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Li, K. Hubacek, and Y. L. Siu, “Wind power in China—dream or reality?” Energy, vol. 37, no. 1, pp. 51–60, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Lazić, G. Pejanović, and M. Živković, “Wind forecasts for wind power generation using the Eta model,” Renewable Energy, vol. 35, no. 6, pp. 1236–1243, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Monfared, H. Rastegar, and H. M. Kojabadi, “A new strategy for wind speed forecasting using artificial intelligent methods,” Renewable Energy, vol. 34, no. 3, pp. 845–848, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. Smart grid, “Wikipedia,” 2012, http://en.wikipedia.org/wiki/Smart_grid.
  14. W. Wang, Y. Xu, and M. Khanna, “A survey on the communication architectures in smart grid,” Computer Networks, vol. 55, no. 15, pp. 3604–3629, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. N. Wu, J. Chen, and L. R. Liu, “Construction of China’s smart grid information system analysis,” Renewable & Sustainable Energy Reviews, vol. 15, pp. 4236–4241, 2011. View at Publisher · View at Google Scholar
  16. J. Gao, Y. Xiao, J. Liu, W. Liang, and C. L. P. Chen, “A survey of communication/networking in Smart Grids,” Future Generation Computer Systems, vol. 28, no. 2, pp. 391–404, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. M. G. de Giorgi, A. Ficarella, and M. Tarantino, “Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods,” Energy, vol. 36, no. 7, pp. 3968–3978, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Guo, J. Zhao, W. Zhang, and J. Wang, “A corrected hybrid approach for wind speed prediction in Hexi Corridor of China,” Energy, vol. 36, no. 3, pp. 1668–1679, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. I. J. Ramírez-Rosado and L. A. Fernández-Jiménez, “An advanced model for short-term forecasting of mean wind speed and wind electric power,” Control and Intelligent Systems, vol. 32, no. 1, pp. 21–26, 2004. View at Google Scholar · View at Scopus
  20. A. Sfetsos, “A novel approach for the forecasting of mean hourly wind speed time series,” Renewable Energy, vol. 27, no. 2, pp. 163–174, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. G. Li and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,” Applied Energy, vol. 87, no. 7, pp. 2313–2320, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Liu, J. Shi, and E. Erdem, “Prediction of wind speed time series using modified Taylor Kriging method,” Energy, vol. 35, no. 12, pp. 4870–4879, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. K. Chen and J. Yu, “Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach,” Applied Energy, vol. 113, pp. 690–705, 2014. View at Publisher · View at Google Scholar
  24. L. Landberg, “Short-term prediction of the power production from wind farms,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 80, no. 1-2, pp. 207–220, 1999. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Negnevitsky and C. W. Potter, “Innovative short-term wind generation prediction techniques,” in Proceedings of the IEEE PES Power Systems Conference and Exposition (PSCE '06), pp. 60–65, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Cassola and M. Burlando, “Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output,” Applied Energy, vol. 99, pp. 154–166, 2012. View at Publisher · View at Google Scholar
  27. M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915–920, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. S. J. Wu and S. L. Lin, “Intelligent web-based fuzzy and grey models for hourly wind speed forecast,” International Journal of Computers, vol. 4, pp. 235–242, 2010. View at Google Scholar
  29. J.-F. Li, B.-H. Zhang, G.-L. Xie, Y. Li, and C.-X. Mao, “Grey predictor models for wind speed-wind power prediction,” Power System Protection and Control, vol. 38, no. 19, pp. 151–159, 2010. View at Google Scholar · View at Scopus
  30. L. Lin, J. T. Eriksson, H. Vihriala, and L. Soderlund, “Predicting wind behavior with neural networks,” in Proceedings the European Wind Energy Conference, pp. 655–658.
  31. M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou, and I. M. Manousaridis, “Short-term forecasting of wind speed and related electrical power,” Solar Energy, vol. 63, no. 1, pp. 61–68, 1998. View at Publisher · View at Google Scholar · View at Scopus
  32. H. G. Beyer, T. Degner, J. Haussmann, M. Hoffman, and P. Rujan, “Short term forecast of wind speed and power output of a wind turbine with neural networks,” in Proceedings of the 2nd European Congress on Intelligent Techniques and Soft Computing, pp. 349–352, 1994.
  33. G. N. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, “Wind power forecasting using advanced neural networks models,” IEEE Transactions on Energy Conversion, vol. 11, no. 4, pp. 762–767, 1996. View at Google Scholar · View at Scopus
  34. A. More and M. C. Deo, “Forecasting wind with neural networks,” Marine Structures, vol. 16, no. 1, pp. 35–49, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. G. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, “A fuzzy logic and neural network based wind power model,” in Proceedings of the European Wind Energy Conference, pp. 596–599, 1996.
  36. X. Wang, G. Sideratos, N. Hatziargyriou, and L. H. Tsoukalas, “Wind speed forecasting for power system operational planning,” in Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems, pp. 470–474, September 2004. View at Scopus
  37. H. Mori and A. Yuihara, “Deterministic annealing clustering for ANN-based short-term load forecasting,” IEEE Transactions on Power Systems, vol. 16, no. 3, pp. 545–551, 2001. View at Publisher · View at Google Scholar · View at Scopus
  38. Z. Xiao, S.-J. Ye, B. Zhong, and C.-X. Sun, “BP neural network with rough set for short term load forecasting,” Expert Systems with Applications, vol. 36, no. 1, pp. 273–279, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. X. Zhang, W. Yan, and Z. Bao, “Short-term load forecasting of power systems by combination of wavelet transform and AMPSO based neural network,” Energy Procedia, vol. 13, pp. 6006–6016, 2011. View at Publisher · View at Google Scholar
  40. A. Badri, Z. Ameli, and A. Birjandi, “Application of artificial neural networks and fuzzy logic methods for short termload forecasting,” Energy Procedia, vol. 14, pp. 1883–1888, 2012. View at Publisher · View at Google Scholar
  41. D.-X. Niu, H.-F. Shi, and D. D. Wu, “Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm,” Applied Soft Computing Journal, vol. 12, no. 6, pp. 1822–1827, 2012. View at Publisher · View at Google Scholar · View at Scopus
  42. H. Y. Yamin, Q. El-Dwairi, and S. M. Shahidehpour, “A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty,” International Journal of Electrical Power & Energy Systems, vol. 29, no. 8, pp. 609–616, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. G. Mokryani and P. Siano, “Evaluating the integration of wind power into distribution networks by using monte carlo simulation,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 244–255, 2013. View at Google Scholar
  44. G. Mokryani and P. Siano, “Combined monte carlo simulation and OPF for wind turbines integration into distribution networks,” Electric Power Systems Research, vol. 103, pp. 37–48, 2013. View at Publisher · View at Google Scholar
  45. G. Mokryani and P. Siano, “Optimal wind turbines placement within a distribution market environment,” Applied Soft Computing, vol. 13, no. 10, pp. 4038–4046, 2013. View at Publisher · View at Google Scholar
  46. R. Vautard and M. Ghil, “Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series,” Physica D: Nonlinear Phenomena, vol. 35, no. 3, pp. 395–424, 1989. View at Google Scholar · View at Scopus
  47. R. Vautard, P. Yiou, and M. Ghil, “Singular-spectrum analysis: a toolkit for short, noisy chaotic signals,” Physica D: Nonlinear Phenomena, vol. 58, no. 1–4, pp. 95–126, 1992. View at Google Scholar · View at Scopus
  48. M. de Carvalho, P. C. Rodrigues, and A. Rua, “Tracking the US business cycle with a singular spectrum analysis,” Economics Letters, vol. 114, no. 1, pp. 32–35, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. H. Hassani, S. Heravi, and A. Zhigljavsky, “Forecasting European industrial production with singular spectrum analysis,” International Journal of Forecasting, vol. 25, no. 1, pp. 103–118, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. 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
  51. W. R. Christiaanse, “Short term load forecasting using general exponential smoothing,” IEEE Transactions on Power Apparatus Systems, vol. 90, no. 2, pp. 900–911, 1971. View at Google Scholar · View at Scopus
  52. S.-J. Huang and K.-R. Shih, “Short-term load forecasting via ARMA model identification including non-Gaussian process considerations,” IEEE Transactions on Power Systems, vol. 18, no. 2, pp. 673–679, 2003. View at Publisher · View at Google Scholar · View at Scopus
  53. 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
  54. Z. Guo, W. Zhao, H. Lu, and J. Wang, “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,” Renewable Energy, vol. 37, no. 1, pp. 241–249, 2012. View at Publisher · View at Google Scholar · View at Scopus
  55. Y. Dong, J. Wang, H. Jiang, and J. Wu, “Short-term electricity price forecast based on the improved hybrid model,” Energy Conversion and Management, vol. 52, no. 8-9, pp. 2987–2995, 2011. View at Publisher · View at Google Scholar · View at Scopus
  56. A.-O. Boudraa and J.-C. Cexus, “EMD-based signal filtering,” IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 6, pp. 2196–2202, 2007. View at Publisher · View at Google Scholar · View at Scopus
  57. H. Hassani, “Singular spectrum analysis: methodology and comparison,” Journal of Data Science, vol. 5, pp. 239–257, 2007. View at Google Scholar
  58. N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques, Chapman & Hall/CRC, 2001.
  59. N. Golyandina and A. Korobeynikov, “Basic singular spectrum analysis and forecasting with R,” Computational Statistics & Data Analysis, vol. 71, pp. 934–954, 2014. View at Google Scholar
  60. M. Claudio and S. Rocco, “Singular spectrum analysis and forecasting of failure time series,” Reliability Engineering & System Safety, vol. 114, pp. 126–136, 2013. View at Publisher · View at Google Scholar
  61. G. Tzagkarakis, M. Papadopouli, and P. Tsakalides, “Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN,” Performance Evaluation, vol. 66, no. 3-5, pp. 173–190, 2009. View at Publisher · View at Google Scholar · View at Scopus
  62. C. A. F. Marques, J. A. Ferreira, A. Rocha et al., “Singular spectrum analysis and forecasting of hydrological time series,” Physics and Chemistry of the Earth, vol. 31, no. 18, pp. 1172–1179, 2006. View at Publisher · View at Google Scholar · View at Scopus
  63. S. M. Sadjadi, Weather Research and Forecasting Model 2. 2 Documentation: A Step-by-step guide of a Model Run, 2007.
  64. K. Afshar and N. Bigdeli, “Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA),” Energy, vol. 36, no. 5, pp. 2620–2627, 2011. View at Publisher · View at Google Scholar · View at Scopus
  65. G. E. P. Box and G. Jenkins, Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, Calif, USA, 1970.
  66. V. Vapnik, The Nature of Statistic Learning Theory, Springer, New York, NY, USA, 1995.
  67. P.-F. Pai and C.-S. Lin, “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega, vol. 33, no. 6, pp. 497–505, 2005. View at Publisher · View at Google Scholar · View at Scopus
  68. Z.-H. Guo, J. Wu, H.-Y. Lu, and J.-Z. Wang, “A case study on a hybrid wind speed forecasting method using BP neural network,” Knowledge-Based Systems, vol. 24, no. 7, pp. 1048–1056, 2011. View at Publisher · View at Google Scholar · View at Scopus