Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation

Abstract

In order to improve the forecasting accuracy of short-term wind speed, a forecasting method based on autoregressive moving average with echo state network compensation is proposed in this article. First, the linear and nonlinear characteristics of short-term wind speed can be determined by Brock–Dechert–Scheinkman statistics method. Then, autoregressive moving average model is used for modeling and to forecast the linear component of short-term wind speed. The linear component of short-term wind speed sequence is obtained. Artificial bee colony algorithm–optimized echo state network model is used as the forecasting model of forecasting error sequences with the nonlinear characteristic. Finally, the final forecasting value is obtained by adding forecasting values of autoregressive moving average model and forecasting error values of echo state network model. k-fold cross-validation is used to improve the generalization ability of the forecasting model. The simulation comparison results show that the proposed forecasting method has higher prediction accuracy with the smaller prediction error. The forecasting indicators are also better than other forecasting methods.