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

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• 作者：Yu JIANG ; Xingying CHEN ; Kun YU…
• 关键词：Hybrid method ; Multi ; step ; ahead prediction ; Wind power forecast ; Boosting algorithm ; Time series model
• 刊名：Journal of Modern Power Systems and Clean Energy
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：5
• 期：1
• 页码：126-133
• 全文大小：1020KB
• 刊物主题：Energy Systems; Renewable and Green Energy; Power Electronics, Electrical Machines and Networks;
• 出版者：Springer Berlin Heidelberg
• ISSN：2196-5420
• 卷排序：5

文摘

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: ①proposing a novel hybrid method using Boosting algorithm and a multi-step forecast approach to improve the forecasting capacity of traditional ARMA model; ②calculating 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.