scholarly journals An improved random forest model of short-term wind-power forecasting to enhance accuracy, efficiency, and robustness

Wind Energy ◽  
2018 ◽  
Vol 21 (12) ◽  
pp. 1383-1394 ◽  
Author(s):  
Kunpeng Shi ◽  
Ying Qiao ◽  
Wei Zhao ◽  
Qing Wang ◽  
Menghua Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Wind Power ◽  
Random Forest Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forest Model ◽  
Power Forecasting

Wind Power Forecasting Based on the BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Engineering Application ◽  
Weather Forecast ◽  
Difficult Problem ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.

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