A Multistep Wind Speed Forecasting System Considering Double Time Series Features
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
... ar 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. INDEX TERMS Data preprocessing, neural networks, wind speed forecasting, wind energy Lei Zhang received the B.from 2008 to 2009.She is currently a professor in the School of Energy and Power Engineering of Inner Mongolia University of Technology. Her main research direction is the development and utilization of new energy, composite energy storage and its control, and offgrid wind and solar hybrid power generation systems. Jianlong Ma received the B.S. and Ph.D. degree in Energy and Power Engineering from Inner Mongolia University of Technology in 2005 and 2014. He received the M.S. degree in Energy Science and Engineering from Harbin Institute of Technology in 2007. He is currently a professor at the School of Energy and Power Engineering of Inner Mongolia University of Technology. His main research direction is wind power blade comprehensive performance optimization design, new airfoil and blade development for wind power installations, and advanced detection and analysis methods for wind power systems. Rihan Hai received the B.S. and M.S. degree of Engineering from Tomsk University of Technology, Russia, in 2015 and 2017. She is currently a teaching assistant in the School of Energy and Power Engineering, Inner Mongolia University of Technology. Her research interests include new energy electric vehicles and new energy power generation technology.