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Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM

Background: Smart grids, characterized by their ability to integrate renewable energy sources and manage the dynamic balance between supply and demand, require sophisticated prediction models to maintain stability. Traditional machine learning (ML) models often fall short in predicting the highly va...

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Principais autores: Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea
Format: Artigo
Sprog:Inglês
Udgivet: Elsevier 2024-12-01
Serier:Results in Engineering
Fag:
Aquila optimizer
Deep learning
Feature selection
Hyperparameter tuning
LSTM
Smart grid
Online adgang:http://www.sciencedirect.com/science/article/pii/S2590123024015159
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