Table of Contents
Journal of Wind Energy
Volume 2014, Article ID 683939, 9 pages
http://dx.doi.org/10.1155/2014/683939
Research Article

A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

1Department of Organic Greenhouse Crops and Floriculture, School of Agricultural Technology, Antikalamos, 24100 Kalamata, Greece
2Department of Electrical and Electronic Engineering Educators, School of Pedagogical and Technological Education (ASPETE), N. Heraklion, 14121 Athens, Greece
3School of Engineering, University of Greenwich, Medway Campus, Pembroke Building, Central Avenue, Chatham Maritime, Kent ME4 4TB, UK

Received 19 March 2014; Accepted 14 May 2014; Published 12 June 2014

Academic Editor: Adrian Ilinca

Copyright © 2014 Stylianos Sp. Pappas 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. 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
  2. M. Marciukaitis, V. Katinas, and A. Kavaliauskas, “Wind power usage and prediction prospects in Lithuania,” Renewable and Sustainable Energy Reviews, vol. 12, no. 1, pp. 265–277, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. C.-H. Wen and C. A. Vassiliadis, “Applying hybrid artificial intelligence techniques in wastewater treatment,” Engineering Applications of Artificial Intelligence, vol. 11, no. 6, pp. 685–705, 1998. View at Google Scholar · View at Scopus
  4. M. Shafie-Khah, M. P. Moghaddam, and M. K. Sheikh-El-Eslami, “Price forecasting of day-ahead electricity markets using a hybrid forecast method,” Energy Conversion and Management, vol. 52, no. 5, pp. 2165–2169, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. K.-Y. Chen, “Combining linear and nonlinear model in forecasting tourism demand,” Expert Systems with Applications, vol. 38, no. 8, pp. 10368–10376, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Baruque, E. Corchado, A. Mata, and J. M. Corchado, “A forecasting solution to the oil spill problem based on a hybrid intelligent system,” Information Sciences, vol. 180, no. 10, pp. 2029–2043, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. W.-C. Yeh, W.-W. Chang, and Y. Y. Chung, “A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method,” Expert Systems with Applications, vol. 36, no. 4, pp. 8204–8211, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. D. G. Lainiotis, “Partitioning: a unifying framework for adaptive systems—I: estimation,” Proceedings of the IEEE, vol. 64, no. 8, pp. 1126–1143, 1976. View at Publisher · View at Google Scholar · View at Scopus
  9. D. G. Lainiotis, “A unifying framework for adaptive systems—II: control,” Proceedings of the IEEE, vol. 64, no. 8, pp. 1182–1198, 1976. View at Publisher · View at Google Scholar · View at Scopus
  10. S. S. Pappas, A. K. Leros, and S. K. Katsikas, “Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory,” Digital Signal Processing, vol. 16, no. 6, pp. 782–795, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. S. K. Katsikas, S. D. Likothanassis, G. N. Beligiannis, K. G. Berketis, and D. A. Fotakis, “Genetically determined variable structure multiple model estimation,” IEEE Transactions on Signal Processing, vol. 49, no. 10, pp. 2253–2261, 2001. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Papaparaskeva, “A partitioned adaptive approach to nonlinear channel equalization demetrios G. lainiotis and,” IEEE Transactions on Communications, vol. 46, no. 10, pp. 1325–1336, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. V. C. Moussas, S. D. Likothanassis, S. K. Katsikas, and A. K. Leros, “Adaptive on-line multiple source detection,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), pp. 1029–1032, March 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. D. G. Lainiotis, K. S. Katsikas, and S. D. Likothanasis, “Adaptive deconvolution of seismic signals—performance, computational analysis, parallelism,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 11, pp. 1715–1734, 1988. View at Publisher · View at Google Scholar · View at Scopus
  15. N. V. Nikitakos, A. K. Leros, and S. K. Katsikas, “Towed array shape estimation using multimodel partitioning filters,” IEEE Journal of Oceanic Engineering, vol. 23, no. 4, pp. 380–384, 1998. View at Publisher · View at Google Scholar · View at Scopus
  16. V. C. Moussas and S. S. Pappas, “Adaptive network anomaly detection using bandwidth utilization data,” in Proceedings of the 1st International Conference on Experiments/Processes/System Modelling/Simulation/Optimization, Athens, Greece, 2005.
  17. S. S. Pappas, L. Ekonomou, P. Karampelas, S. K. Katsikas, and P. Liatsis, “Modeling of the grounding resistance variation using ARMA models,” Simulation Modelling Practice and Theory, vol. 16, no. 5, pp. 560–570, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. S. S. Pappas, L. Ekonomou, D. C. Karamousantas, G. E. Chatzarakis, S. K. Katsikas, and P. Liatsis, “Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models,” Energy, vol. 33, no. 9, pp. 1353–1360, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. S. S. Pappas, L. Ekonomou, P. Karampelas et al., “Electricity demand load forecasting of the Hellenic power system using an ARMA model,” Electric Power Systems Research, vol. 80, no. 3, pp. 256–264, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. E. Peter Carden and J. M. W. Brownjohn, “ARMA modelled time-series classification for structural health monitoring of civil infrastructure,” Mechanical Systems and Signal Processing, vol. 22, no. 2, pp. 295–314, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Chen, H. Zhao, and L. Yu, “Demand forecasting in automotive aftermarket based on ARMA model,” in Proceedings of the International Conference on Management and Service Science (MASS '10), pp. 1–4, 2010.
  22. Z. Jie, X. Limei, and L. Lin, “Equipment fault forecasting based on ARMA model,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA '07), pp. 3514–3518, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Majhi, S. Mishra, B. Majhi, G. Panda, and M. Rout, “Efficient sales forecasting using PSO based adaptive ARMA model,” in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 1333–1337, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Li and P. Luan, “ARMA model for predicting the number of new outbreaks of newcastle disease during the month,” in Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE '11), vol. 4, pp. 660–663, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. S. S. Pappas, L. Ekonomou, V. C. Moussas, P. Karampelas, and S. K. Katsikas, “Adaptive load forecasting of the Hellenic electric grid,” Journal of Zhejiang University: Science A, vol. 9, no. 12, pp. 1724–1730, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. G. Beligiannis, L. Skarlas, and S. Likothanassis, “A generic applied evolutionary hybrid technique,” IEEE Signal Processing Magazine, vol. 21, no. 3, pp. 28–38, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. S. S. Pappas, N. Harkiolakis, P. Karampelas, L. Ekonomou, and S. K. Katsikas, “A new algorithm for on-line Multivariate ARMA identification using Multimodel Partitioning Theory,” in Proceedings of the 12th Pan-Hellenic Conference on Informatics (PCI '08), pp. 222–226, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. V. C. Moussas, S. K. Katsikas, and D. G. Lainiotis, “Adaptive estimation of FCG using nonlinear state-space models,” Stochastic Analysis and Applications, vol. 23, no. 4, pp. 705–722, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. V. Vapnik, The Nature of Statistic Learning Theory, Springer, New York, NY, 1995.