@inproceedings{5bdb99354d274411833fd83eaa3082be,
title = "A generalized data preprocessing method for wind power prediction",
abstract = "A generalized data preprocessing method is proposed in this paper to reduce the amount of outliers among historical data and further improve the power prediction accuracy. Historical data of wind farms are fit with an S-shape curve via Linear Regression Model. Based on this statistical curve, outliers can be identified considering different fitting error. Furthermore, the expansion of wind farm is identified through the number of outliers. Then a selection method for the allowed maximum fitting errors is recommended. The presented method has been integrated into the prediction system in Inner Mongolia of China with 36 farms. The actual application shows that the wind farm power prediction accuracy has been improved by at least 28\% with this model. It is noteworthy that the proposed preprocessing method is just based on statistical analysis of historical data and thus compatible with various wind power prediction methods.",
keywords = "Allowed fitting errors, S-shaped statistical curve, data preprocessing, outliers, samples update, wind farm expansion",
author = "Jiakun An and Zhaohong Bie and Xiaozhong Chen and Bowen Hua and Shiyu Liu",
year = "2013",
doi = "10.1109/PESMG.2013.6672644",
language = "英语",
isbn = "9781479913039",
series = "IEEE Power and Energy Society General Meeting",
booktitle = "2013 IEEE Power and Energy Society General Meeting, PES 2013",
note = "2013 IEEE Power and Energy Society General Meeting, PES 2013 ; Conference date: 21-07-2013 Through 25-07-2013",
}
