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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 260351, 6 pages
http://dx.doi.org/10.1155/2013/260351
Research Article

Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network

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

Received 20 March 2013; Revised 3 September 2013; Accepted 3 September 2013

Academic Editor: Chuandong Li

Copyright © 2013 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. Niu, Y. Wei, Y. Shi, and H. R. Karimi, “A novel evaluation model for hybrid power system based on vague set and Dempster-Shafer evidence theory,” Mathematical Problems in Engineering, Article ID 784389, 12 pages, 2012. View at Google Scholar · View at MathSciNet
  2. D. Niu, Y. Wei, and L. Fan, “Social comprehensive benefit evaluation for hybrid power system including thermal power, wind power and photovoltaic power,” Energy Education Science and Technology B, vol. 5, no. 2, pp. 1113–1120, 2013. View at Google Scholar
  3. R.-J. Wai and C.-Y. Lin, “Active low-frequency ripple control for clean-energy power-conditioning mechanism,” IEEE Transactions on Industrial Electronics, vol. 57, no. 11, pp. 3780–3792, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. I.-S. Kim, M.-B. Kim, and M.-J. Youn, “New maximum power point tracker using sliding-mode observer for estimation of solar array current in the grid-connected photovoltaic system,” IEEE Transactions on Industrial Electronics, vol. 53, no. 4, pp. 1027–1035, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Yona, T. Senjyu, T. Funabashi, et al., “Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system,” Intelligent Systems Applications to Power Systems, vol. 1-2, pp. 423–428, 2007. View at Google Scholar
  6. H. Zhao, Z. Ren, and W. Huang, “Short-term load forecasting considering weekly period based on periodical auto regression,” Proceedings of the Chinese Society of Electrical Engineering, vol. 17, no. 3, pp. 211–216, 1997. View at Google Scholar · View at Scopus
  7. X. Li, M. Luo, and K. Feng, “Comparison of neural network methods for short-term load forecasting,” Relay, vol. 35, no. 6, pp. 49–53, 2007. View at Google Scholar
  8. A. Prias, B. Buchenel, and Y. Jaccard, “Heterogeneous artificial neural network for short term electrical load forecasting,” IEEE Transactions on Power Systems, vol. 11, no. 1, pp. 456–463, 1996. View at Google Scholar
  9. A. Yona, T. Senjyu, and T. Funabashi, “Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system,” in Proceedings of the IEEE Power Engineering Society General Meeting, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Guo, J. Liu, Y. Ma, et al., “Optimal algorithm of electric power system’s short-term load forecasting based on radial function neural network,” Power System Protection and Control, vol. 36, no. 23, pp. 45–48, 2008. View at Google Scholar
  11. C. Kang, A. Zhou, P. Wang, et al., “Impact analysis of hourly factors in short-term load forecasting and its processing strategy,” Power System Technology, vol. 30, no. 7, pp. 5–11, 2006. View at Google Scholar
  12. X. Liu, D. Luo, J. Yao, et al., “Short-term load forecasting based on load decomposition and hourly weather factors,” Power System Technology, vol. 33, no. 12, pp. 94–100, 2009. View at Google Scholar
  13. H. Qin, W. Wang, H. Zhou, et al., “Short-term electric load forecast using human body amenity indicator,” Proceedings of the CSU-EPSA, vol. 18, no. 2, pp. 63–66, 2006. View at Google Scholar
  14. D. Wu, “Discussion on various formulas for forecasting human comfort index,” Meteological Science and Technology, vol. 31, no. 6, pp. 370–372, 2003. View at Google Scholar
  15. A. Sinha, “Short term load forecasting using artificial neural network,” IEEE Transactions on Power Systems, pp. 548–553, 2000. View at Google Scholar
  16. C. Kang, Q. Xia, and B. Zhang, “Review of power system load forecasting and its development,” Automation of Electric Power Systems, vol. 28, no. 17, pp. 1–11, 2004. View at Google Scholar · View at Scopus
  17. H. Yoo and R. Pimmel, “Short term load forecasting using a self-supervised adaptive neural network,” IEEE Transactions on Power Systems, vol. 14, no. 2, pp. 779–784, 1999. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Wu and H. Yan, “Short-term load forecasting technique based on adaptive optimal fuzzy logic system,” Automation of Electric Power Systems, vol. 23, no. 17, pp. 35–37, 1999. View at Google Scholar · View at Scopus
  19. J. Xie and G. Tang, “Fuzzy inference forecasting of power load with self-learning function,” Journal of Southeast University, vol. 28, no. 4, pp. 156–160, 1998. View at Google Scholar
  20. D. Singh and S. P. Singh, “Self organization and learning methods in short term electric load forecasting: a review,” Electric Power Components and Systems, vol. 30, no. 10, pp. 1075–1089, 2002. View at Publisher · View at Google Scholar · View at Scopus