Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
Joint Authors
Hu, Zhongyi
Xiong, Tao
Bao, Yukun
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well.
Among the existing forecasting models, support vector regression (SVR) has gained much attention.
Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model.
In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA.
Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.
American Psychological Association (APA)
Hu, Zhongyi& Bao, Yukun& Xiong, Tao. 2013. Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1032754
Modern Language Association (MLA)
Hu, Zhongyi…[et al.]. Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms. The Scientific World Journal No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1032754
American Medical Association (AMA)
Hu, Zhongyi& Bao, Yukun& Xiong, Tao. Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1032754
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032754