Abstract:

The problems of randomness and non-linearity of wind speed make it difficult to predict wind power.For the prediction of wind power,the prediction methods of ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) were proposed.First,the original wind speed signal is modal decomposed,and the EEMD decomposes the wind speed signal into multiple characteristic modal components and a residual component to effectively optimize the nonlinear characteristics of the signal.Second,the SVR model is trained according to the component signals obtained by the decomposition to realize the component prediction.Finally,the predicted components are combined to determine the wind speed prediction sequence,and the predicted power is obtained from the conversion relationship between wind speed and power.Through the case simulation,the prediction effect of the EEMD-SVR model is verified and the model compared and analyzed.The results show that the model can achieve reliable decomposition of non-stationary sequences,and the wind power prediction effect is effectively improved.

Key words: wind power prediction, wind speed prediction, EEMD, SVR, combined model