Short-term wind power combined prediction based on EWT-SMMKL methods

• Autorzy: Li Jun , Ma Liancai

Identyfikatory

DOI

10.24425/aee.2021.138262

• Języki publikacji: EN

Abstrakty

EN

Since wind power generation has strong randomness and is difficult to predict, a class of combined prediction methods based on empirical wavelet 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) kernel- based 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.

Słowa kluczowe

EN

combined model; empirical wavelet transform; prediction; soft margin multiple kernel learning; wind power

PL

model łączony; empiryczna transformacja falkowa; przewidywanie; miękki margines uczenia wielokrotnego jądra; energia wiatrowa

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2021
• Tom: Vol. 70, nr 4
• Strony: 801--817
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wz.

Twórcy

autor

Li Jun

• Lanzhou Jiaotong University Lanzhou, Gansu 730070, China

• https://orcid.org/0000-0002-1406-2734+

autor

Ma Liancai

• Lanzhou Jiaotong University Lanzhou, Gansu 730070, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).