Cargando…
CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany
The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-qualit...
Autores principales: | Aksan, Fachrizal, Li, Yang, Suresh, Vishnu, Janik, Przemysław |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9864294/ https://www.ncbi.nlm.nih.gov/pubmed/36679696 http://dx.doi.org/10.3390/s23020901 |
Ejemplares similares
-
Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods
por: Mohammed Alsumaidaee, Yaseen Ahmed, et al.
Publicado: (2023) -
CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM
por: Gao, Guohong, et al.
Publicado: (2023) -
Solar Power Prediction Using Dual Stream CNN-LSTM Architecture
por: Alharkan, Hamad, et al.
Publicado: (2023) -
Utilizing CNN-LSTM techniques for the enhancement of medical systems
por: Rayan, Alanazi, et al.
Publicado: (2023) -
NLOS Identification in WLANs Using Deep LSTM with CNN Features
por: Nguyen, Viet-Hung, et al.
Publicado: (2018)