Very short-term probabilistic wind power forecasting based on Markov chain models

2010 ◽  
Author(s):  
A. Carpinon ◽  
R. Langella ◽  
A. Testa ◽  
M. Giorgio
Keyword(s):  
Markov Chain ◽  
Wind Power ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Markov Chain Models ◽  
Power Forecasting

scholarly journals Markov chain modeling for very-short-term wind power forecasting

2015 ◽  
Vol 122 ◽  
pp. 152-158 ◽  
Author(s):  
A. Carpinone ◽  
M. Giorgio ◽  
R. Langella ◽  
A. Testa
Keyword(s):  
Markov Chain ◽  
Wind Power ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Markov Chain Modeling ◽  
Power Forecasting

scholarly journals Spatio-temporal Markov chain model for very-short-term wind power forecasting

2019 ◽  
Vol 2019 (18) ◽  
pp. 5018-5022 ◽  
Author(s):  
Yongning Zhao ◽  
Lin Ye ◽  
Zheng Wang ◽  
Linlin Wu ◽  
Bingxu Zhai ◽  
...  
Keyword(s):  
Markov Chain ◽  
Wind Power ◽  
Markov Chain Model ◽  
Chain Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Spatio Temporal ◽  
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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