Forecasting the stability of A 4-node architecture smart grid using machine learning

Basit Öğe Kaydı
dc.authorid0000-0002-0271-6868
dc.authorid0000-0002-9735-5697
dc.authorid0000-0003-2989-3781
dc.contributor.authorHangün, Batuhan
dc.contributor.authorEyecioğlu, Önder
dc.contributor.authorBeken, Murat
dc.date.accessioned2024-09-25T19:59:59Z
dc.date.available2024-09-25T19:59:59Z
dc.date.issued2022
dc.departmentBAİBÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü en_US
dc.description10th International Conference on Smart Grid (icSmartGrid) -- JUN 27-29, 2022 - Istanbul, TURKEYen_US
dc.description.abstractSmart grid stability is one of the most important factors that can be used as a criterion for assessing the usability of smart grid architecture, so testing and predicting stability under various circumstances hold great importance. As a result of the increase in residential and industrial structures, and the integration of renewable energy into the smart grids, some intelligent solutions to predict stability to prevent unwanted instabilities in a future smart grid architecture is needed. In this study, we used various machine learning methods to predict smart grid stability. We approached the problem as a classification problem, we used a 4-node architecture smart grid dataset, and applied some well-known classification methods to classify the dataset into two classes which are stable and unstable. For the classification part, we used k-Nearest Neighbour (kNN), neural networks (NN), a support vector machine (SVM), and a decision tree. All four methods were tested under different hyper parameters. Finally, the ones with the best results were reported.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.doi10.1109/ICSMARTGRID55722.2022.9848635
dc.identifier.endpage442en_US
dc.identifier.isbn978-1-6654-8605-7
dc.identifier.isbn978-1-6654-8604-0
dc.identifier.scopus2-s2.0-85137796114en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage440en_US
dc.identifier.urihttps://doi.org/10.1109/ICSMARTGRID55722.2022.9848635
dc.identifier.urihttps://hdl.handle.net/20.500.12491/14024
dc.identifier.wosWOS:001267854400040en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEyecioğlu, Önder
dc.institutionauthorBeken, Murat
dc.institutionauthorid0000-0002-9735-5697
dc.institutionauthorid0000-0003-2989-3781
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2022 10th International Conference on Smart Grid, Icsmartgriden_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectSmart Griden_US
dc.subjectStabilityen_US
dc.subjectMachine Learningen_US
dc.subjectClassificationen_US
dc.subjectIntelligent Solutions
dc.subjectHyper Parameters
dc.titleForecasting the stability of A 4-node architecture smart grid using machine learningen_US
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
batuhan-hangün.pdf
Boyut:
272.42 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam metin/Full text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu