Secondary Hybrid Decomposition Strategy for Wind Power Prediction Using Long Short-Term Memory With Crisscross Optimization

Secondary Hybrid Decomposition Strategy for Wind Power Prediction Using Long Short-Term Memory With Crisscross Optimization

This paper presents a hybrid forecasting model for a wind power named Secondary Hybrid Decomposition (SHD)-Long Short-Term Memory (LSTM) with Crisscross Optimization (CSO).The model integrates three key techniques to improve prediction accuracy. Firstly, the SHD mitigates the limitations of single-s...

Full description

Saved in:
Bibliographic Details
Main Authors: Yoseph Mekonnen Abebe, Habtamu Kassa Bayu, Tekalign Tesfaye Mengistu, Abera Tullu, Sunghun Jung
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Crisscross optimization
empirical mode decomposition
forecasting
long short-term memory
secondary hybrid decomposition
Online Access:https://ieeexplore.ieee.org/document/11122439/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/11122439/

Similar Items