Novel double-layer bidirectional LSTM network with improved attention mechanism for predicting energy consumption.

He, Yan-Lin; Chen, Lei; Gao, Yanlu; Ma, Jia-Hui; Xu, Yuan; Zhu, Qun-Xiong

He, Yan-Lin; Chen, Lei; Gao, Yanlu; Ma, Jia-Hui; Xu, Yuan; Zhu, Qun-Xiong.

Afiliación

He YL; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Chen L; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Gao Y; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Ma JH; School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China.
Xu Y; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China. Electronic address: xuyuan@mail.buct.edu.cn.
Zhu QX; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China. Electronic address: zhuqx@mail.buct.edu.cn.

ISA Trans ; 127: 350-360, 2022 Aug.

Article en En | MEDLINE | ID: mdl-34493381

Palabras clave

Attention mechanism; Bi-LSTM; Energy consumption; Particle swarm optimization; Time series

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: ISA Trans Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google