Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

作     者：Wang, Chen Wu, Jie Wang, Jianzhou Hu, Zhongjin 

作者机构：Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China Northwest Univ Nationalities Sch Math & Comp Sci Lanzhou 730030 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China 

出 版 物：《MATHEMATICAL PROBLEMS IN ENGINEERING》 (Math. Probl. Eng.)

年 卷 期：2016年第2016卷第1期

页      面：1-17页

核心收录：

学科分类：08[工学] 0701[理学-数学] 

主　　题：WIND speed measurements ELECTRONIC data processing SUPPORT vector machines COMPUTER algorithms HILBERT-Huang transform 

摘      要：Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: ( I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition ( EMD), which reduces the effect of noise on the wind speed data;( II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine ( SVM) model are optimized by the cuckoo search ( CS) algorithm;( III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent ( SD) method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small rootmean squared errors and mean absolute percentage errors.