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dc.contributor.authorIbrar, Muhammad
dc.contributor.authorHassan, Muhammad Awais
dc.contributor.authorShaukat, Kamran
dc.contributor.authorAlam, Talha Mahboob
dc.contributor.authorKhurshid, Khaldoon Syed
dc.contributor.authorHameed, Ibrahim A.
dc.contributor.authorAljuaid, Hanan
dc.contributor.authorLuo, Suhuai
dc.date.accessioned2023-01-20T08:50:39Z
dc.date.available2023-01-20T08:50:39Z
dc.date.created2022-11-10T11:03:33Z
dc.date.issued2022
dc.identifier.citationWireless Communications & Mobile Computing. 2022, 2022 .en_US
dc.identifier.issn1530-8669
dc.identifier.urihttps://hdl.handle.net/11250/3044827
dc.description.abstractA decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost-sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.en_US
dc.language.isoengen_US
dc.publisherHindawien_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resourcesen_US
dc.title.alternativeA Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resourcesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.volume2022en_US
dc.source.journalWireless Communications & Mobile Computingen_US
dc.identifier.doi10.1155/2022/2697303
dc.identifier.cristin2071666
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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