A Multistep Wind Speed Forecasting System Considering Double Time Series Features

Abstract

Multistep wind speed forecasting is of great significance for wind energy utilization. To further improve the performance of multistep wind speed forecasting, a new multistep forecasting strategy is proposed based on the basic wind speed multistep forecasting strategy and the corresponding forecasting system is established. The system consists of three parts, including a wavelet decomposition preprocessing module, nonlinear autoregressive artificial neural network and nonlinear autoregressive exogenous artificial neural network composite prediction module, and support vector machine classifier postprocessing module. The system analyzes historical wind speed data from two different angles to obtain dual time series features. Then, the double time series feature is used to improve the accuracy and stability of the multipart forecast. The predictive performance of the system is verified through two experiments. Experiment I tests the prediction accuracy and computing resource consumption of the system. Experiment II tests the stability of the system prediction under different wind conditions. The results show that, compared with the traditional multistep prediction strategy system, the new system has a better prediction accuracy and more stable prediction performance.