Earticle

초록

영어

Wind power prediction accuracy is important for assessing the security and economy when wind power is connected to the grid, and wind speed is the key factor. This article presents a future four hours prediction scheme that combined chaos phase space reconstruction with NWP method. Historical wind speed data are reconstructed as phase space vectors, which are used as the first input part of prediction model, and the NWP data at the prediction time as the second input part. Wind speed at the height of turbine hub is derived from neural network model output. To test the approach, the data from a wind farm are used for this study. The prediction results are presented and compared separately to the chaos neural network model, NWP ANN model and persistence model. The results show that the method presented in this paper has higher prediction precision.

목차

Abstract
 1. Introduction
 2. Relations between Wind Speed and wind Turbine Output Power
 3. Neural Network Prediction based on Chaos Phase Space Reconstruction
  3.1 Chaos Phase Space Reconstruction.
  3.2 GRNN Prediction Model based on Chaos Phase Space Reconstruction
 4. Neural Network Prediction based on NWP
 5. Hybrid Forecasting Model
  5.1 Sample Introduction and Data Preprocessing
  5.2 Model Training and Prediction
  5.3 Predicted Results Comparison
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Yang Gao [ Shenyang Institute of Engineering, Shenyang, China ]
• Aoran Xu [ Shenyang Institute of Engineering, Shenyang, China ]
• Yan Zhao [ Shenyang Institute of Engineering, Shenyang, China ]
• Baogui Liu [ Shenyang Institute of Engineering, Shenyang, China ]
• Liu Zhang [ Shenyang Institute of Engineering, Shenyang, China ]
• Lei Dong [ Shenyang Institute of Engineering, Shenyang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605