Wind Power Forecasting Based on Improved Biased Wavelet Neural Network

Yu Han Mao

doi:10.4028/www.scientific.net/AMR.915-916.1532

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 915-916Wind Power Forecasting Based on Improved Biased...

Wind Power Forecasting Based on Improved Biased Wavelet Neural Network

Abstract:

Wind power prediction is the key to grid-connected wind power system. In this paper, first of all, we decompose and reconstruct the power sequence by wavelet analysis, and reduce the noise of the detail signal, to obtain the strong-regularity subsequence. We adapt the biased wavelet neural network rolling forecast model for the processed sequence to obtain seven days of rolling forecast results through several amendments. For the sequence of 5 minutes interval the prediction accuracy is 98.63%, for the sequence of 15 minutes interval the prediction accuracy is 99.88%.

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Periodical:

Advanced Materials Research (Volumes 915-916)

Pages:

1532-1535

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.915-916.1532

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Online since:

April 2014

Authors:

Yu Han Mao*

Keywords:

Biased Wavelet, Neural Network (NN), Wavelet Analysis

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* - Corresponding Author

References

[1] JinhuaHuang HuiPeng. Neural network method to predict the short-term wind power of wind farms, Electrical & Electronics，2009，9，p.57-p.60 In Chinese.

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[2] Hongtao Shi. A Short-term Wind Power Prediction Method Based on Wavelet De composition and BP Neural Network , Automation of Electric Power Systems, 2011，16，p.44-p.48 In Chinese.

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