@ARTICLE{Li_Jun_Short-term_2021,
 author={Li, Jun and Ma, Liancai},
 volume={vol. 70},
 number={No 4},
 journal={Archives of Electrical Engineering},
 pages={801-817},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences},
 abstract={Since wind power generation has strong randomness and is difficult to predict, a class of combined prediction methods based on empiricalwavelet transform(EWT) and soft margin multiple kernel learning (SMMKL) is proposed in this paper. As a new approach to build adaptive wavelets, the main idea is to extract the different modes of signals by designing an appropriate wavelet filter bank. The SMMKL method effectively avoids the disadvantage of the hard margin MKL method of selecting only a few base kernels and discarding other useful basis kernels when solving for the objective function. Firstly, the EWT method is used to decompose the time series data. Secondly, different SMMKL forecasting models are constructed for the sub-sequences formed by each mode component signal. The training processes of the forecasting model are respectively implemented by two different methods, i.e., the hinge loss soft margin MKL and the square hinge loss soft margin MKL. Simultaneously, the ultimate forecasting results can be obtained by the superposition of the corresponding forecasting model. In order to verify the effectiveness of the proposed method, it was applied to an actual wind speed data set from National Renewable Energy Laboratory (NREL) for short-term wind power single-step or multi-step time series indirectly forecasting. Compared with a radial basic function (RBF) kernelbased support vector machine (SVM), using SimpleMKL under the same condition, the experimental results show that the proposed EWT-SMMKL methods based on two different algorithms have higher forecasting accuracy, and the combined models show effectiveness.},
 type={Article},
 title={Short-term wind power combined prediction based on EWT-SMMKL methods},
 URL={http://www.czasopisma.pan.pl/Content/121561/PDF/art05.pdf},
 doi={10.24425/aee.2021.138262},
 keywords={combined model, empirical wavelet transform, prediction, soft margin multiple kernel learning, wind power},
}