scholarly journals Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 63352-63365 ◽  
Author(s):  
Zhichao Shi ◽  
Hao Liang ◽  
Venkata Dinavahi
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wavelet Neural Network ◽  
Short Term ◽  
Interval Prediction

Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

2020 ◽  
Vol 213 ◽  
pp. 112869 ◽  
Author(s):  
Sinvaldo Rodrigues Moreno ◽  
Ramon Gomes da Silva ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Speed Forecasting ◽  
Decomposition Model ◽  
Multi Stage ◽  
Long Short Term Memory

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

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