@inproceedings{86c4e020e6d14d8ab7dc53142410fd81,
title = "Long-term wind power prediction based on rough set",
abstract = "In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.",
keywords = "Long-term prediction, Neural network, Rough set, Wind power prediction",
author = "Shuang Gao and Lei Dong and Liao, {Xiao Zhong} and Yang Gao",
year = "2013",
doi = "10.4028/www.scientific.net/AMM.329.411",
language = "English",
isbn = "9783037857236",
series = "Applied Mechanics and Materials",
pages = "411--415",
booktitle = "Advanced Technologies on Measure and Diagnosis, Manufacturing Systems and Environment Engineering",
note = "3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013 ; Conference date: 22-03-2013 Through 24-03-2013",
}