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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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024015159 |
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| author | Safwan Mahmood Al-Selwi Mohd Fadzil Hassan Said Jadid Abdulkadir Mohammed Gamal Ragab Alawi Alqushaibi Ebrahim Hamid Sumiea |
| author_facet | Safwan Mahmood Al-Selwi Mohd Fadzil Hassan Said Jadid Abdulkadir Mohammed Gamal Ragab Alawi Alqushaibi Ebrahim Hamid Sumiea |
| author_sort | Safwan Mahmood Al-Selwi |
| collection | DOAJ |
| description | 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 variable nature of smart grid operations. Methods: This study introduces an ensemble stacked bidirectional Long Short-Term Memory model enhanced by a proposed Adaptive Aquila Optimizer (AAO). The AAO uses a Sigmoid Factor to balance exploration and exploitation, adapting the transition from broad searches to focused ones based on iteration progress. It is utilized for feature selection by identifying and excluding irrelevant and redundant features and methodically evaluates seven key hyperparameters to fine-tune the model's performance. Additionally, a weighted voting mechanism is employed to aggregate predictions in the ensemble model. Results: Multiple rounds of empirical experiments using different sets of optimizers and configurations, supported by the visualization capabilities of TensorBoard, demonstrate significant improvements in the performance of the AAO-BiLSTM model. The results show profound potential with accuracy, precision, recall, and F1-score rates of 99.55%, surpassing both traditional ML algorithms and state-of-the-art approaches. |
| format | Article |
| id | doaj-art-e3ff781fc5014e629b1de4448b0c3c02 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-e3ff781fc5014e629b1de4448b0c3c022025-08-20T01:58:16ZengElsevierResults in Engineering2590-12302024-12-012410326110.1016/j.rineng.2024.103261Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTMSafwan Mahmood Al-Selwi0Mohd Fadzil Hassan1Said Jadid Abdulkadir2Mohammed Gamal Ragab3Alawi Alqushaibi4Ebrahim Hamid Sumiea5Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Center for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Corresponding author at: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia.Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Center for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, MalaysiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Center for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, MalaysiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Center for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, MalaysiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Center for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, MalaysiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, MalaysiaBackground: 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 variable nature of smart grid operations. Methods: This study introduces an ensemble stacked bidirectional Long Short-Term Memory model enhanced by a proposed Adaptive Aquila Optimizer (AAO). The AAO uses a Sigmoid Factor to balance exploration and exploitation, adapting the transition from broad searches to focused ones based on iteration progress. It is utilized for feature selection by identifying and excluding irrelevant and redundant features and methodically evaluates seven key hyperparameters to fine-tune the model's performance. Additionally, a weighted voting mechanism is employed to aggregate predictions in the ensemble model. Results: Multiple rounds of empirical experiments using different sets of optimizers and configurations, supported by the visualization capabilities of TensorBoard, demonstrate significant improvements in the performance of the AAO-BiLSTM model. The results show profound potential with accuracy, precision, recall, and F1-score rates of 99.55%, surpassing both traditional ML algorithms and state-of-the-art approaches.http://www.sciencedirect.com/science/article/pii/S2590123024015159Aquila optimizerDeep learningFeature selectionHyperparameter tuningLSTMSmart grid |
| spellingShingle | Safwan Mahmood Al-Selwi Mohd Fadzil Hassan Said Jadid Abdulkadir Mohammed Gamal Ragab Alawi Alqushaibi Ebrahim Hamid Sumiea Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM Results in Engineering Aquila optimizer Deep learning Feature selection Hyperparameter tuning LSTM Smart grid |
| title | Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM |
| title_full | Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM |
| title_fullStr | Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM |
| title_full_unstemmed | Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM |
| title_short | Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM |
| title_sort | smart grid stability prediction using adaptive aquila optimizer and ensemble stacked bilstm |
| topic | Aquila optimizer Deep learning Feature selection Hyperparameter tuning LSTM Smart grid |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024015159 |
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