Wind power prediction using time-series analysis base on rough sets

In long-term prediction, dealing with the relevant factors correctly is the key point to improve the wind power prediction accuracy. The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test...

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Bibliographic Details
Main Authors: Gao Shuang, Dong Lei, Tian Chengwei, Liao Xiaozhong
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Chaos
Information systems
neural network
Numerical models
prediction model
Predictive models
rough set
Wind power generation
wind power prediction
Wind speed
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Summary:In long-term prediction, dealing with the relevant factors correctly is the key point to improve the wind power prediction accuracy. The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test the approach, the weather data from Beijing area are used for this study. The prediction results are presented and compared to the chaos neural network model and persistence model. The results show that rough set method will be a useful tool in longterm prediction of wind power.
DOI:10.1109/ICEICE.2011.5777058