A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine
Year: 2013
Type: article
Abstract: This paper studied the short-term prediction of wind speed by means of wavelet decomposition and Extreme Learning Machine. Wind speed signal was decomposed into several sequences by wavelet decomposition to reduce the non-stationary. Secondly, the phase space reconstructed was used to mine sequences characteristics, and then an improved extreme learning machine model of each component was established. Finally, the results of each component forecast superimposed to get the final result. The simulation result verified that the hybrid model effectively improved the wind speed prediction accuracy.
Source: Advanced materials research
Authors: Yi Hui Zhang, He Wang, Zhi Jian Hu, Kai Wang, Yan Li, Dong Shan Huang, Wen Hui Ning, Cheng Xue Zhang
Institution: Wuhan University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2018991955