Publication:
Comparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithms

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dc.citedby1
dc.contributor.authorBhoyar P.en_US
dc.contributor.authorRahman M.S.A.en_US
dc.contributor.authorIrfan S.A.en_US
dc.contributor.authorAmirulddin U.A.U.en_US
dc.contributor.authorid58522765200en_US
dc.contributor.authorid58811982000en_US
dc.contributor.authorid57192382110en_US
dc.contributor.authorid26422804600en_US
dc.date.accessioned2024-10-14T03:20:56Z
dc.date.available2024-10-14T03:20:56Z
dc.date.issued2023
dc.description.abstractLightning 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/APL57308.2023.10181898
dc.identifier.scopus2-s2.0-85166732425
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85166732425&doi=10.1109%2fAPL57308.2023.10181898&partnerID=40&md5=a193079f71fe5fcc0dd3eac8496a79a6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34593
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleAPL 2023 - 12th Asia-Pacific International Conference on Lightning
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.subjecthybrid optimization
dc.subjectLightning nowcasting
dc.subjectComputational efficiency
dc.subjectDeep learning
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectComparative analyzes
dc.subjectDeep learning
dc.subjectFault analysis
dc.subjectHybrid optimization
dc.subjectLearning models
dc.subjectLightning nowcasting
dc.subjectNowcasting
dc.subjectPeak currents
dc.subjectPrediction-based
dc.subjectRandom forests
dc.subjectLightning
dc.titleComparative Analysis of Peak Current Prediction based on Random Forest and MLP Neural Network Algorithmsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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