Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network
Abstract As the world’s energy structure is gradually changing, the automotive industry is shifting its focus to new energy vehicles in an effort to improve the performance and service life of the charging pile. To solve the problem that traditional models tend to fall into locally optimal solutions...
Saved in:
| Main Authors: | Yansheng Huang, Atthapol Ngaopitakkul, Suntiti Yoomak |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
|
| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-025-00530-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Charging pile recommendation method for idle electric taxis based on recurrent neural network
by: Jian JIA, et al.
Published: (2020-12-01) -
Optimization of Analog Circuit Parameters Using Bidirectional Long Short-Term Memory Coupled with an Enhanced Whale Optimization Algorithm
by: Hengfei Yang, et al.
Published: (2024-12-01) -
Research on the Error Estimation Method for Electric Energy Meters of Electric Vehicle Charging Piles based on Deep Learning
by: Wang Juan, et al.
Published: (2025-04-01) -
Study of the Group Controlling on Large-Capacity DC Charging Piles for Peak Load Regulation
by: Zuping ZHANG, et al.
Published: (2019-02-01) -
Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU
by: LU Jing, et al.
Published: (2025-05-01)