Abstract
In order to forecast short-term wind speed more accurately to reduce the negative impact on the whole grid effectively, a hybrid model combining wavelet analysis and RBF neural network is proposed in this chapter. By introducing wavelet decomposition and single branch reconstruction, the original wind speed sequence can be decomposed to each frequency subsequence which has stronger regularity. Meanwhile, it can solve the problem of local optimization according to the ACF of each subsequence in the process of modeling. The case analysis shows that the hybrid model has higher forecasting precision than the single RBF one, which lays a good foundation for the short-term power forecasting.
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Huang, Xb., Mao, Pl., Dong, Xp., Tang, Hy. (2014). A Hybrid Model for Short-Term Wind Speed Forecasting Based on Wavelet Analysis and RBF Neural Network. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_19
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DOI: https://doi.org/10.1007/978-1-4614-4981-2_19
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