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Abstract
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Article Information:
Short-Term Wind Power Prediction and Comprehensive Evaluation based on Multiple Methods
Zhaowei Wang, Jiajie Zhang and Haiyan Wang
Corresponding Author: Zhaowei Wang
Submitted: January 31, 2013
Accepted: February 22, 2013
Published: December 25, 2013 |
Abstract:
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Firstly, this study used prediction methods, including Kalman filter method, the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model and the BP neural network model based on time sequence, to predict real-timely the wind power. And then, we construct indexes such as mean absolute error, root-mean-square error, accuracy rate and percent of pass to have error analysis on the predictive effect and get the best results of prediction effect that based on time sequence of the BP neural network model. Finally, we concluded the universal rule between the relative prediction error of single typhoon electric unit power of and the prediction relative error of total machine power by the analysis into lateral error indicators. And we analyze the influence on the error of the prediction result that resulting from the converge of wind generator power.
Key words: BP neural network model, GARCH model, kalman filter method, wind power prediction, , ,
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Cite this Reference:
Zhaowei Wang, Jiajie Zhang and Haiyan Wang, . Short-Term Wind Power Prediction and Comprehensive Evaluation based on Multiple Methods. Research Journal of Applied Sciences, Engineering and Technology, (24): 4615-4620.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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