Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model

Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model

The accurate forecast of wind power is crucial for the stable operation and economic dispatch of renewable energy power systems. To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term...

Full description

Saved in:
Bibliographic Details
Main Authors: Weilong Yu, Shuaibing Li, Hao Zhang, Yongqiang Kang, Hongwei Li, Haiying Dong
Format: Article
Language:English
Published: Tsinghua University Press 2024-12-01
Series:iEnergy
Subjects:
wind turbines
ultra-short-term power forecasting
convolutional neural network
bidirectional long short-term memory network
attention mechanism
subtraction-average-based optimizer
Online Access:https://www.sciopen.com/article/10.23919/IEN.2024.0026
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.sciopen.com/article/10.23919/IEN.2024.0026

Similar Items