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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Κύριοι συγγραφείς:	Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	Elsevier 2024-12-01
Σειρά:	Results in Engineering
Θέματα:	Aquila optimizer Deep learning Feature selection Hyperparameter tuning LSTM Smart grid
Διαθέσιμο Online:	http://www.sciencedirect.com/science/article/pii/S2590123024015159
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