A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting

2017 ◽  
Vol 54 ◽  
pp. 296-312 ◽  
Author(s):  
Xuejiao Ma ◽  
Yu Jin ◽  
Qingli Dong
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Spectrum Analysis ◽  
Fuzzy Neural Network ◽  
Singular Spectrum Analysis ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Brain Storm Optimization ◽  
Singular Spectrum ◽  
Fuzzy Neural

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.

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

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