An error-revision-based method for very short-term wind speed prediction using wavelet transform and support vector machine

Autor:	Lang Chengyu, Xuewei Duan, Ruiqi Wang, Shumin Sun, Yong Zhang, Qingquan Li
Rok vydání:	2015
Předmět:	Data processing Wind power Computer science business.industry Wavelet transform computer.software_genre Wind speed Data modeling Support vector machine Electric power system Hyperparameter optimization Data mining business Algorithm computer
Zdroj:	ICCAIS
DOI:	10.1109/iccais.2015.7338661
Popis:	With the rapid growth of wind power connected to the power system, the problems caused by the volatile nature of wind speed have drawn more and more attention from system operator and researchers. The effective wind speed and power prediction of is the key to solve all these problems. This paper proposed a method based on prediction error revision, which use wavelet transform and Grid Search optimized SMO-SVM during data processing. The simulation indicate that the proposed method performs better than the traditional forecasting model on one and three step ahead prediction.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::61eaaa38ab0ee97007c8257b83dc1911 https://doi.org/10.1109/iccais.2015.7338661 Zobrazit plný text záznamu