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Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model

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dc.citedby1
dc.contributor.authorKhaleefah S.H.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorKhattak U.F.en_US
dc.contributor.authorJubair M.A.en_US
dc.contributor.authorAfyenni R.en_US
dc.contributor.authorid57188929678en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57193278880en_US
dc.contributor.authorid57203690245en_US
dc.contributor.authorid57204624661en_US
dc.date.accessioned2025-03-03T07:45:33Z
dc.date.available2025-03-03T07:45:33Z
dc.date.issued2024
dc.description.abstractA smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%. ? 2024, Politeknik Negeri Padang. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.62527/joiv.8.2.2758
dc.identifier.epage1098
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85207002445
dc.identifier.spage1091
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207002445&doi=10.62527%2fjoiv.8.2.2758&partnerID=40&md5=49bf6f7848ee098427e03d911e5fb256
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36892
dc.identifier.volume8
dc.pagecount7
dc.publisherPoliteknik Negeri Padangen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal on Informatics Visualization
dc.titleOptimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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