An improved wind power forecasting model based on eliminating outliers. Issue 1 (February 2016)

• Record Type: Journal Article
• Authors: Guo, Jingxin; Zhang, Xiao-Yu; Jang, Wenling; Wang, Hongqing
• Is Part Of: Wind engineering. Volume 40:Issue 1(2016)
• Page Start: 50
• Page End: 58
• Publication Date: 2016-02
• Subjects: Wind power -- pointwise elimination rule -- stopping-parameter -- short-term forecasting model; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X15624345
• Languages: English
• ISSNs: 0309-524X

Abstract

In this paper, we develop an improved outlier pointwise elimination rule based on the wind-power observation data and prove that when the stopping-parameter isw = ( t 1 − α 2 ( m − 1 ) ) / ( m − 1 ), the abnormal data can be eliminated maximally, whereα is the confidence level and m is the number of observation data. Then, by integrating the exponential smoothing method with the time series and the Kalman filter, we obtain an improved wind-power short-term forecasting model which has a high accuracy. Finally, a wind-power numerical experiment shows the simulation results and illustrates the effectiveness of our method.