Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

作     者：Jiang, Yu Chen, Xingying Yu, Kun Liao, Yingchen 

作者机构：Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China 

出 版 物：《JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY》 (J. Mod. Power Syst. Clean Energy)

年 卷 期：2017年第5卷第1期

页      面：126-133页

核心收录：

基　　金：National High Technology Research and Development of China (863 Program) [2012AA050214] National Natural Science Foundation of China State Grid Corporation of China (Impact research of source-grid-load interaction on operation and control of future power system) 

主　　题：Hybrid method Multi-step-ahead prediction Wind power forecast Boosting algorithm Time series model 

摘      要：Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: (1)proposing a novel hybrid method using Boosting algorithm and a multi-step forecast approach to improve the forecasting capacity of traditional ARMA model;(2)calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.