A Generalized Data Preprocessing Method For Wind Power Prediction

被引：0
|
作者：
An, Jiakun [1 ]
Bie, Zhaohong [1 ]
Chen, Xiaozhong [1 ]
Hua, Bowen [1 ]
Liu, Shiyu [2 ]
机构：
[1] Xi An Jiao Tong Univ, Dept Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian 710049, Peoples R China
[2] Hohai Univ, Dept Elect Engn, Nanjing, Peoples R China
来源：
2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES) | 2013年
基金：
国家高技术研究发展计划(863计划);
关键词：
Allowed fitting errors; data preprocessing; outliers; samples update; S-shaped statistical curve; wind farm expansion; SPEED;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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.
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收藏
页数：5
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