Wind speed prediction based on singular spectrum analysis and neural network structural learning

2020 ◽  
Vol 216 ◽  
pp. 112956 ◽  
Author(s):  
Xiwei Mi ◽  
Shuo Zhao
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Structural Learning ◽  
Wind Speed Prediction ◽  
Singular Spectrum ◽  
Speed Prediction

scholarly journals Multistep Wind Speed Forecasting Using a Novel Model Hybridizing Singular Spectrum Analysis, Modified Intelligent Optimization, and Rolling Elman Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Zhongshan Yang ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Monitoring And Control ◽  
Elman Neural Network ◽  
Intelligent Optimization ◽  
Wind Speed Forecasting ◽  
Singular Spectrum

Wind speed high-accuracy forecasting, an important part of the electrical system monitoring and control, is of the essence to protect the safety of wind power utilization. However, the wind speed signals are always intermittent and intrinsic complexity; therefore, it is difficult to forecast them accurately. Many traditional wind speed forecasting studies have focused on single models, which leads to poor prediction accuracy. In this paper, a new hybrid model is proposed to overcome the shortcoming of single models by combining singular spectrum analysis, modified intelligent optimization, and the rolling Elman neural network. In this model, except for the multiple seasonal patterns used to reduce interferences from the original data, the rolling model is utilized to forecast the multistep wind speed. To verify the forecasting ability of the proposed hybrid model, 10 min and 60 min wind speed data from the province of Shandong, China, were proposed in this paper as the case study. Compared to the other models, the proposed hybrid model forecasts the wind speed with higher accuracy.

Sign in / Sign up

Export Citation Format

Share Document