Earticle

초록

영어

Wind power prediction of wind farm plays a decisive role in stable electric power system operation.The BP neural network’s basic principle was introduced, and the numerical weather prediction (NWP) data and power data of wind farm as the training data of BP neural network was selected and trained; a linear regression model about the sample prediction error was presented, which considers the coupling relationship between the individual sample prediction error, the individual sample prediction error of BP neural network was selected as the regression factor, the individual sample prediction result of BP neural network was modified. As the modified prediction results performing, the prediction algorithm of short-term wind power considering the sample prediction error correction, has good self-learning and adaptive ability of BP neural network. It has overcome the shortcoming that the BP neural network has only considered the overall the prediction error of training samples, but without considered the prediction error of individual samples. This has further improved the prediction accuracy of BP neural network.

목차

Abstract
 1. Introduction
 2. BP Neural Network and its Improvement
  2.1. The Theory of BP Neural Network
  2.2. The Improvement of BP Neural Network
 3. Model Establishing and Data Processing
 4. Network Training and Data Analysis
 5. The Linear Regression Model of Individual Sample Prediction Error
 6. Conclusion
 References

키워드

저자

• Gu Bo [ School of Electric Power, North China University of Water Conservancy and Electric Power, Zhengzhou, 450011, China ]
• Hu Hao [ School of Electric Power, North China University of Water Conservancy and Electric Power, Zhengzhou, 450011, China ]
• Liu Xinyu [ School of Electric Power, North China University of Water Conservancy and Electric Power, Zhengzhou, 450011, China ]
• Zhang Hongtao [ School of Electric Power, North China University of Water Conservancy and Electric Power, Zhengzhou, 450011, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605