A novel combination prediction model of ultra-short-term wind speed

Abstract

The accurate prediction of ultra-short-term wind speed has important theoretical significance and practical application value. In this paper, a combination prediction model of ultra-short-term wind speed based on variational mode decomposition is proposed. Firstly, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed and obtain the components of different frequency. The approximate entropy algorithm is used to calculate the complexity of each component. According to the calculation results, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is used to predict low complexity components. Then, the predicted values of each component are added to get the final prediction result. Gray wolf algorithm is used to optimize the model parameters of support vector machine and echo state network. In addition, the approximate entropy calculation results show that compared with the original ultra-short-term wind speed, the complexity of the components obtained by the variational mode decomposition algorithm is reduced, which makes the modeling of the ultra-short-term wind speed system simpler. Finally, the validity of the model is verified by taking the ultra-short-term wind speed data of 5 minutes and 10 minutes sampling period actually collected as the research object. The results show that the prediction model proposed in this paper has better performance than other single or combination prediction models.