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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
Joint Authors
Wang, Chen
Hu, Zhongjin
Wang, Jianzhou
Wu, Jie
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-19
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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 root mean squared errors and mean absolute percentage errors.
American Psychological Association (APA)
Wang, Chen& Wu, Jie& Wang, Jianzhou& Hu, Zhongjin. 2016. 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. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1112242
Modern Language Association (MLA)
Wang, Chen…[et al.]. 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. Mathematical Problems in Engineering No. 2016 (2016), pp.1-17.
https://search.emarefa.net/detail/BIM-1112242
American Medical Association (AMA)
Wang, Chen& Wu, Jie& Wang, Jianzhou& Hu, Zhongjin. 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. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1112242
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112242