基于经验模态分解法优化支持向量机模型的日前风电功率组合预测

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Abstract In order to solve the problem of wind power output forecast in wind farm, a combined short-term wind power forecast method based on empirical mode decomposition and support vector machine (SVM) algorithm is applied. First empirical mode decomposition method is used to decompose wind power data sequence into a series of relatively stable component, in order to reduce the mutual influence between different characteristic information, and then the optimization of support vector machine method is adopted for the decomposition of each component of the sequence prediction model, respectively, for each component characteristics choose different kernel functions and parameters in order to obtain the best prediction accuracy of a single component. Finally, the prediction results of each component of superposition forms the projections wind power in the end. Numerical examples show that the combined algorithm is more accurate than other single forecasting methods.

Key words： empirical mode decomposition support vector machine (SVM) wind power combination forecast

Received: 02 September 2019 Published: 20 March 2020

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	Xia Shuyue
	Dong Xinyi

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Xia Shuyue,Dong Xinyi. Day-ahead wind power combination prediction based on empirical mode decomposition method to optimize support vector machine model[J]. Electrical Engineering, 2020, 21(3): 11-15.

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http://dqjs.cesmedia.cn/EN/Y2020/V21/I3/11

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