scholarly journals A Combined Short-Term Forecast Model of Wind Power Based on Empirical Mode Decomposition and Augmented Dickey-Fuller Test

2021 ◽  
Vol 2022 (1) ◽  
pp. 012017
Author(s):  
Jin Wang ◽  
Tianyao Ji ◽  
Mengshi Li
Keyword(s):  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Forecast Model ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Mode Decomposition ◽  
Augmented Dickey Fuller

scholarly journals Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN

2013 ◽  
Vol 2 (2) ◽  
pp. 192-199 ◽  
Author(s):  
Zeng-Wei Zheng ◽  
Yuan-Yi Chen ◽  
Xiao-Wei Zhou ◽  
Mei-Mei Huo ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Short-Term Forecast and Error Analysis of Wind Power Based on Weight Factors

2014 ◽  
Vol 1008-1009 ◽  
pp. 137-143
Author(s):  
Ying Jie Qin ◽  
Shan Song ◽  
Bin Shi ◽  
Zhen Jian Xie ◽  
Li Wei Qiao
Keyword(s):  
Wind Power ◽  
Wind Velocity ◽  
Large Scale ◽  
Power Grid ◽  
Real Data ◽  
Power Prediction ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Weight Factors

For power grid with large-scale wind energy, the short-term wind power prediction is important to the grid’s scheduling and stable operating. The overall short-term forecast for wind power connected to the grid relies on the wind velocity and historical power data. Firstly, K-means clustering is introduced to model the power grid, so that the relationship between wind velocity and power can be perfectly described. Considering that there are multiple factors contributing to the prediction of wind velocity and power, we use real data of 15 wind generating set to obtain dependable weight factors of all those dimensions. With the support of mass data, the prediction of power is proved by several measurements (ME, MRE, MAE, RMSE) to be accurate.

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