A Wind Speed Prediction Method Based on Improved Empirical Mode Decomposition and Support Vector Machine. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Wang, Shibo; Guo, Yongchao; Wang, Yanzhuo; Li, Qinghua; Wang, Nan; Sun, Shumin; Cheng, Yan; Yu, Peng
• Is Part Of: IOP conference series. Volume 680:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/680/1/012012
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bat algorithm (BA) to optimize the support vector machine, this paper proposed a combined model for short-term wind speed forecasting to predict the wind speed more accurately. Firstly, CEEMDAN was used to decompose the original wind speed time series into a series of subsequences with different frequencies. Secondly, the decomposed subsequences were forecasted by combined model of BA-SVM. Finally, the wind speed forecasting results was achieved by superposing each predicted subsequence. The simulation results suggest that the model improves the prediction accuracy and reduces the error.