期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2014
卷号：7
期号：3
页码：355-362
DOI：10.14257/ijhit.2014.7.3.33
出版社：SERSC
摘要：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.
关键词：Wind Power Prediction; Numerical Weather Prediction; BP Neural Network; ; Prediction Error
