Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion.

Duan, Jiandong; Tian, Xuan; Ma, Wentao; Qiu, Xinyu; Wang, Peng; An, Lin

Duan, Jiandong; Tian, Xuan; Ma, Wentao; Qiu, Xinyu; Wang, Peng; An, Lin.

Afiliación

Duan J; School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Tian X; State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710048, China.
Ma W; School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Qiu X; School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Wang P; School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
An L; School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.

Entropy (Basel) ; 21(7)2019 Jul 19.

Article en En | MEDLINE | ID: mdl-33267421

Palabras clave

electricity consumption forecasting; mixture maximum correntropy criterion; parameter optimization; support vector regression

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Entropy (Basel) Año: 2019 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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