NDLI: Short-term wind speed prediction based on support vector machine of fuzzy information granulation

Content Provider	IEEE Xplore Digital Library
Author	Xiao Cheng Peng Guo
Copyright Year	2013
Description	Author affiliation: Sch. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China (Xiao Cheng; Peng Guo)
Abstract	Wind power prediction is an effective way to decrease the effects for the large-scale grid-connected wind power generation. Improving accuracy of short-term wind prediction is the key to wind power prediction. There is often a lot of redundant information in the observed values of wind speed to result in large computation and affect the predictive validity. In this paper, a support vector machine (SVM) method was proposed based on information granulating. Firstly, original data was dealt with fuzzy information granulation to form information granulation. Secondly, adopting SVM is widely used in regression prediction to predict the changes of short-term average wind speed in trends and in space. At last, the actual data was compared with the predicted data to verify results. The verification experiments show that granulated data can not only reflect the characteristics of wind but also reduce redundant information. The predicted result was changing in the space of average wind speed. So, this method can predict the short-term wind speed space.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	1918
Ending Page	1923
File Size	256447
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561247
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Short-term Prediction Wind speed Wind Speed Wind power generation Wind farms Predictive models Information Granulation Forecasting Support Vector Machines
Content Type	Text
Resource Type	Article

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