Interval Prediction of Wind Power Based on Bivariate Empirical Mode Decomposition and Least Squares Support Vector Machine

doi:10.3969/j.issn.1000-7229.2019.05.014

Abstract

Abstract: Accurate prediction of wind power is an important measure to solve the problem that large-scale wind power accesses in power grid. At present， there are still large errors in the wind power prediction. An interval prediction method of wind power based on bivariate empirical mode decomposition and least squares support vector machine is proposed to help solve the problem. Firstly， a scale factor rule is proposed to build complex-valued power intervals. Then， the upper and lower bound of wind power are decomposed by bivariate empirical mode decomposition and reconstructed by sample entropy to extract main features. In addition， a model combined least squares support vector machine with deep belief network of each component is established. Finally， the overall interval prediction with a certain confidence level is obtained by superimposing the corresponding results. Taking real power data of a wind farm as examples， the results demonstrate that the proposed method can carry out power interval prediction， improving the interval coverage probability to get higher accuracy compared with the existing interval prediction methods.

Key words: wind power, interval forecast, power prediction, empirical mode decomposition

CLC Number:

TM 76

YANG Deyou， GAO Zi′ang， LI Yinxuan. Interval Prediction of Wind Power Based on Bivariate Empirical Mode Decomposition and Least Squares Support Vector Machine[J]. Electric Power Construction, 2019, 40(5): 118-127.

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