Enhancing smart grid efficiency: a modified ANN-LSTM approach for energy storage and distribution optimization
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Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks, Long Short-Term Memory (LSTM), Machine Learning, Predictive Modeling
Kaynak
7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Mohammed, 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.
