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Abstract
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Article Information:
Wind Power Prediction Investigation
Yuanlong Liu, Yuanbiao Zhang and Ziyue Chen
Corresponding Author: Yuanlong Liu
Submitted: July 31, 2012
Accepted: September 03, 2012
Published: February 11, 2013 |
Abstract:
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Daily and real-time forecast data of wind power is predicted in this study using three methods, which are Kalman filter model, GARCH model and time-series-based BP neural network model. Then, owing to evaluation to the calculation of accuracy and qualification rate, the best method, the time-series-based BP neural network model, was selected for its highest accuracy. Moreover, the prediction error influence due to convergence of wind turbine is on consideration according to the evaluation. Finally, suggestions of improving the prediction accuracy were put forward based on the discussion of accuracy-obstacle factors.
Key words: BP neutral network, Kalman filter, time sequence prediction, wind-power prediction, , ,
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Cite this Reference:
Yuanlong Liu, Yuanbiao Zhang and Ziyue Chen, . Wind Power Prediction Investigation. Research Journal of Applied Sciences, Engineering and Technology, (05): 1762-1768.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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