Research Article | OPEN ACCESS
Wind Power Prediction Investigation
1Yuanlong Liu, 2Yuanbiao Zhang and 2Ziyue Chen
1Department of Electrical and Information Engineering
2Department of Packaging Engineering, Mathematical Modeling Innovative
Practice Base, Jinan University, Zhuhai 519070, China
Research Journal of Applied Sciences, Engineering and Technology 2013 5:1762-1768
Received: July 31, 2012 | Accepted: September 03, 2012 | Published: February 11, 2013
Abstract
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.
Keywords:
BP neutral network, Kalman filter, time sequence prediction, wind-power prediction,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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