Wind Power Ramp Events Classification and Forecasting: A Data Mining Approach

被引：0
|
作者：
Zareipour, Hamidreza [1 ]
Huang, Dongliang [1 ]
Rosehart, William [1 ]
机构：
[1] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB T2N 1N4, Canada
来源：
2011 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING | 2011年
关键词：
Wind power; ramp event; data mining; classification; feature selection; MUTUAL INFORMATION;
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：
Available wind power forecasting tools predict the future values of wind power production. System operators use those predictions to estimate the severity of wind power ramp up/down events, and determine the set of actions needed to manage those events. In this paper, a direct approach for predicting the severity of wind power ramp events is presented. Ramp events are categorized into 'classes', and available data are used to predict the class of future ramps. Support vector machines (SVM) are used as classifiers and an elaborate model for forming the set of inputs to the classifier is proposed. Numerical results based on the wind power data in Alberta, Canada, is presented.
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页数：3
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