Quantum computing approach to smart grid stability forecasting

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dc.authorid0000-0002-0271-6868
dc.authorid0000-0002-9735-5697
dc.authorid0000-0001-8823-3148
dc.authorscopusid57215429867
dc.authorscopusid18037100700
dc.authorscopusid24477080000
dc.contributor.authorHangün, Batuhan
dc.contributor.authorEyecioğlu, Önder
dc.contributor.authorAltun, Oğuz
dc.date.accessioned2024-09-25T19:42:51Z
dc.date.available2024-09-25T19:42:51Z
dc.date.issued2024
dc.departmentBAİBÜ, Teknoloji Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionPower Electronics in Everything (PEiE); TMEiCen_US
dc.description12th International Conference on Smart Grid, icSmartGrid 2024 -- 27 May 2024 through 29 May 2024 -- Hybrid, Setubal -- 200813en_US
dc.description.abstractThe stability of a smart grid architecture is one of the most important parameters to assess its effectiveness and reliability. As a result of increased industrialization and use of renewable energy, predicting stability with respect to different scenarios becomes increasingly important to mitigate potential instabilities. Therefore, employing machine learning approaches to forecast system stability is crucial for sustainability. Traditionally, classical computers have been employed for machine learning-based methods; however, the proliferation of producers, consumers, and prosumers in newly built smart grid structures generates massive volumes of data, posing significant challenges for traditional computational methods in processing and analyzing these data efficiently. Quantum computing offers a promising solution for handling large amounts of data generated by smart grids, potentially leading to more accurate and efficient stability predictions. This study investigates a benchmark-based approach by comparing classical machine learning with a quantum machine learning technique to predict the stability of the smart grid. We utilized the "Electrical Grid Stability Simulated Data"dataset, focusing on a 4-node architecture smart grid. We approached this task as a classification problem, comparing the performance of a classical Support Vector Machine (SVM) model with a quantum machine learning approach, namely the Variational Quantum Classifier (VQC). Both methods were tested and evaluated on the basis of their ability to classify the grid's stability into "stable"and "unstable"categories. Our experimental results demonstrate the potential advantages and limitations of quantum computing in improving smart grid stability forecasting, and energy research in general. © 2024 IEEE.en_US
dc.identifier.doi10.1109/icSmartGrid61824.2024.10578114
dc.identifier.endpage843en_US
dc.identifier.isbn979-835036161-2
dc.identifier.scopus2-s2.0-85199483867en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage840en_US
dc.identifier.urihttps://doi.org/10.1109/icSmartGrid61824.2024.10578114
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12305
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEyecioğlu, Önder
dc.institutionauthorid0000-0002-9735-5697
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof12th International Conference on Smart Grid, icSmartGrid 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectClassificationen_US
dc.subjectMachine Learningen_US
dc.subjectQuantum Computingen_US
dc.subjectQuantum Neural Networken_US
dc.subjectSmart Griden_US
dc.subjectStabilityen_US
dc.subjectSVMen_US
dc.subjectVariational Quantum Classifieren_US
dc.titleQuantum computing approach to smart grid stability forecastingen_US
dc.typeConference Objecten_US

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