An improved wind power forecasting model based on eliminating outliers. Issue 1 (February 2016) - Guo, Jingxin; Zhang, Xiao-Yu; Jang, Wenling; Wang, Hongqing
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.