Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms
Lightning events have significant impacts on power systems, infrastructure, and the environment. Accurate and timely nowcasting of lightning occurrences is crucial for effective fault analysis and mitigation. This paper presents the development of a hybrid optimization-based deep learning model for...
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Institute of Electrical and Electronics Engineers Inc.
2024
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| _version_ | 1833352044683984896 |
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| author | Bhoyar P. Rahman M.S.A. Irfan S.A. Amirulddin U.A.U. |
| author2 | 58522765200 |
| author_facet | 58522765200 Bhoyar P. Rahman M.S.A. Irfan S.A. Amirulddin U.A.U. |
| author_sort | Bhoyar P. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Lightning events have significant impacts on power systems, infrastructure, and the environment. Accurate and timely nowcasting of lightning occurrences is crucial for effective fault analysis and mitigation. This paper presents the development of a hybrid optimization-based deep learning model for lightning nowcasting, aiming to improve the accuracy and efficiency of lightning prediction. The objectives include the development of a deep learning model utilizing lightning data, spatial prediction of lightning events within a 1 km diameter, investigating the model's capability for predicting specific time intervals and optimizing the computational cost and prediction accuracy. The proposed model demonstrates enhanced predictive capabilities and optimized computational efficiency, highlighting the potential of AI-driven techniques in lightning nowcasting and fault analysis applications. � 2023 IEEE. |
| format | Conference Paper |
| id | my.uniten.dspace-34593 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-345932024-10-14T11:20:56Z Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms Bhoyar P. Rahman M.S.A. Irfan S.A. Amirulddin U.A.U. 58522765200 58811982000 57192382110 26422804600 artificial intelligence deep learning hybrid optimization Lightning nowcasting Computational efficiency Deep learning Forecasting Learning systems Comparative analyzes Deep learning Fault analysis Hybrid optimization Learning models Lightning nowcasting Nowcasting Peak currents Prediction-based Random forests Lightning Lightning events have significant impacts on power systems, infrastructure, and the environment. Accurate and timely nowcasting of lightning occurrences is crucial for effective fault analysis and mitigation. This paper presents the development of a hybrid optimization-based deep learning model for lightning nowcasting, aiming to improve the accuracy and efficiency of lightning prediction. The objectives include the development of a deep learning model utilizing lightning data, spatial prediction of lightning events within a 1 km diameter, investigating the model's capability for predicting specific time intervals and optimizing the computational cost and prediction accuracy. The proposed model demonstrates enhanced predictive capabilities and optimized computational efficiency, highlighting the potential of AI-driven techniques in lightning nowcasting and fault analysis applications. � 2023 IEEE. Final 2024-10-14T03:20:56Z 2024-10-14T03:20:56Z 2023 Conference Paper 10.1109/APL57308.2023.10181898 2-s2.0-85166732425 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166732425&doi=10.1109%2fAPL57308.2023.10181898&partnerID=40&md5=a193079f71fe5fcc0dd3eac8496a79a6 https://irepository.uniten.edu.my/handle/123456789/34593 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | artificial intelligence deep learning hybrid optimization Lightning nowcasting Computational efficiency Deep learning Forecasting Learning systems Comparative analyzes Deep learning Fault analysis Hybrid optimization Learning models Lightning nowcasting Nowcasting Peak currents Prediction-based Random forests Lightning Bhoyar P. Rahman M.S.A. Irfan S.A. Amirulddin U.A.U. Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms |
| title | Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms |
| title_full | Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms |
| title_fullStr | Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms |
| title_full_unstemmed | Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms |
| title_short | Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms |
| title_sort | comparative analysis of peak current prediction based on random forest and mlp neural network algorithms |
| topic | artificial intelligence deep learning hybrid optimization Lightning nowcasting Computational efficiency Deep learning Forecasting Learning systems Comparative analyzes Deep learning Fault analysis Hybrid optimization Learning models Lightning nowcasting Nowcasting Peak currents Prediction-based Random forests Lightning |
| url_provider | http://dspace.uniten.edu.my/ |