@inproceedings{c70e53f303cd4ddf8675ae0cea41cf25,
title = "Wind power prediction using time-series analysis base on rough sets",
abstract = "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 long-term prediction of wind power.",
keywords = "neural network, prediction model, rough set, wind power prediction",
author = "Shuang Gao and Lei Dong and Chengwei Tian and Xiaozhong Liao",
year = "2011",
doi = "10.1109/ICEICE.2011.5777058",
language = "English",
isbn = "9781424480395",
series = "2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings",
pages = "2847--2852",
booktitle = "2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings",
note = "2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 ; Conference date: 15-04-2011 Through 17-04-2011",
}