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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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Main Authors: Bhoyar P., Rahman M.S.A., Irfan S.A., Amirulddin U.A.U.
Other Authors: 58522765200
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
artificial intelligence
deep learning
hybrid optimization
Lightning nowcasting
Computational efficiency
Deep learning
Forecasting
Learning systems
Comparative analyzes
Fault analysis
Hybrid optimization
Learning models
Nowcasting
Peak currents
Prediction-based
Random forests
Lightning
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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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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
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
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.
author2 58522765200
author_facet 58522765200
Bhoyar P.
Rahman M.S.A.
Irfan S.A.
Amirulddin U.A.U.
format Conference Paper
author Bhoyar P.
Rahman M.S.A.
Irfan S.A.
Amirulddin U.A.U.
author_sort Bhoyar P.
title 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_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_sort comparative analysis of peak current prediction based on random forest and mlp neural network algorithms
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061063136608256
score 13.214268

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