Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty, multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed. Th...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c9b0de10f3ce483d9de93f8dcddb7708 |
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| Sumario: | Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty, multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed. Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection. Firstly, the variational mode decomposition (VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy (mRMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network (BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine (LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy. |
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