Probabilistic Interval Prediction of Wind Power

2015 ◽  
Vol 740 ◽  
pp. 429-432
Author(s):  
Mao Yang ◽  
Gang Du ◽  
Li Sun

As wind power generation rapid development in china, wind power prediction is the key to the system operate safely. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. this paper based on the point forecast, calculate wind power prediction error, formulate the distribution of prediction error, you can get the historical probabilistic distribution of prediction error, use the distribution of error to build the risk assessment of wind power after prediction, give the fluctuate range of predicted values. Probabilistic interval forecasting can obtain the probably of power system operation safely and reliability assessment criterion.

2013 ◽  
Vol 321-324 ◽  
pp. 838-841
Author(s):  
Qiang Wang ◽  
Yang Yang

In order to diminish the effect of reconstructed parameters to prediction of chaotic, a combined model for wind power prediction based on multi-dimension embedding is proposed. The combined model makes use of neural network method to achieve combination of several neural networks models based on phase space reconstruction, which can synthesize information and fuse prediction deviation in different embedding dimension, resulting in forecast accuracy improved. Simulation is performed to the real power time series Fujin wind farm. The results show that the combined prediction model is effective, and the prediction error of neural network combination is less than 7%.


2013 ◽  
Vol 448-453 ◽  
pp. 1851-1857
Author(s):  
Rui Ma ◽  
Ling Ling Wang ◽  
Shu Ju Hu

The prediction accuracy of wind power is important to the power system operation. Based on BP neural network used to forecast directly and time-series method used to forecast indirectly, the output wind power prediction of 4 hours in advance was studied in this paper. Simulation results showed that the performance of direct prediction is better, and the reason for that was analyzed in the paper. Finally, error analysis of prediction was researched. Comprehensive evaluation of prediction error which contains horizontal and longitudinal error evaluation was proposed.


Author(s):  
Gao Yang ◽  
Shu Xinlei ◽  
Liu Baoliang ◽  
Sun Wenzhong ◽  
Zhao Mingjiang ◽  
...  

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