Enhancing smart grid efficiency: a modified ANN-LSTM approach for energy storage and distribution optimization

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dc.contributor.authorMohammed, Ramzi Qasim
dc.contributor.authorAbdulrazzaq, Mohammed Majid
dc.contributor.authorMohammed, Ayoob Jasim
dc.contributor.authorMardikyan, Kevork
dc.contributor.authorÇevik, Mesut
dc.date.accessioned2023-12-21T09:09:28Z
dc.date.available2023-12-21T09:09:28Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe smart grid represents a paradigm shift in energy management, aiming to optimize energy storage and distribution while accommodating the growing demand for renewable energy sources. In this paper, we investigate the application of a modified Artificial Neural Network with Long Short-Term Memory (ANN-LSTM) in addressing the multifaceted challenges of the smart grid. Through rigorous experimentation and simulation, the ANN-LSTM is evaluated in four diverse scenarios, including normal operation, fluctuating renewable energy, peak demand, and grid instability. The results showcase the model's exceptional predictive accuracy, low Mean Squared Error (MSE), and rapid response times, outperforming other models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), Decision Tree (DT), and Fuzzy Logic. Our findings underscore the ANN-LSTM's potential to revolutionize energy storage and distribution in the smart grid, ushering in a new era of efficiency, sustainability, and resilience in energy management.en_US
dc.identifier.citationMohammed, R. Q., Abdulrazzaq, M. M., Mohammed, A. J., Mardikyan, K., & Çevık, M. (2023, October). Enhancing smart grid efficiency: a modified ANN-LSTM approach for energy storage and distribution optimization. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-5). IEEE.en_US
dc.identifier.isbn9798350342154
dc.identifier.scopus2-s2.0-85179139343
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4400
dc.indekslendigikaynakScopus
dc.institutionauthorMardikyan, Kevork
dc.institutionauthorÇevik, Mesut
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 - Proceedings
dc.relation.isversionof10.1109/ISMSIT58785.2023.10304846en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectLong Short-Term Memory (LSTM)en_US
dc.subjectMachine Learningen_US
dc.subjectPredictive Modelingen_US
dc.titleEnhancing smart grid efficiency: a modified ANN-LSTM approach for energy storage and distribution optimization
dc.typeConference Object

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