Abstract
Wind power industry developed rapidly in recent years. wind power is a type of power with randomness and fluctuation. Accurate wind speed forecasting can reduce the impact of wind power. Paper analyzed the wind speed signal with wavelet packet decomposition method from low-frequency and high-frequency, selected the optimal wavelet tree through the principle of minimum entropy. Short-term wind speed prediction model is built with support vector machine regression. This algorithm has advanced and better accuracy by comparing the results.
The Ministry of science and technology support program(2011BAA04B03).
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© 2011 Springer-Verlag Berlin Heidelberg
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Zeng, D., Liu, Y., Liu, J., Liu, J. (2011). Short-Term Wind Speed Forecast Based on Best Wavelet Tree Decomposition and Support Vector Machine Regression. In: Lee, G. (eds) Advances in Automation and Robotics, Vol. 2. Lecture Notes in Electrical Engineering, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25646-2_49
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DOI: https://doi.org/10.1007/978-3-642-25646-2_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25645-5
Online ISBN: 978-3-642-25646-2
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