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Optimizing Smart Grid Performance with Deep Learning Models

Author(s) V. A. G. Raju, Bhyri Rohit Kumar, Sanjeevu Sai Kumar, Pujari Sravya, Ippili Bhanuprakash
Country India
Abstract The smart grid is a cutting-edge power system idea that balances communication and electricity in
system networks. It gives producers, operators, and consumers access to real-time information. The need
to effectively manage electricity distribution to different consuming domains, including homes,
businesses, industries, and smart cities, is growing. In this regard, dynamic power demand must be met
by a stable smart grid system. Because there are so many affecting factors, predicting the stability of the
smart grid is still difficult. Participation from producers and consumers is crucial since determining their
level of involvement can help maintain grid stability. In this study, we suggest a deep learning model for
smart grid stability prediction that is based on Gated Recurrent Units (GRU). Other conventional
machine learning and deep learning classifiers. Such as Recurrent Neural Networks (RNN), Long ShortTerm Memory (LSTM), and Artificial Neural Networks (ANN), are contrasted with the outcomes of the
suggested GRU model. With a 97.45% accuracy rate, our suggested GRU model outperforms previous
models in predicting the stability of the smart grid.
Keywords Smart Grid Dataset, Decision Tree Classifier, Logistic regression, Artificial Neural Networks, recurrent Neural Networks, Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Units.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-02
Cite This Optimizing Smart Grid Performance with Deep Learning Models - V. A. G. Raju, Bhyri Rohit Kumar, Sanjeevu Sai Kumar, Pujari Sravya, Ippili Bhanuprakash - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.4423
DOI https://doi.org/10.71097/IJSAT.v16.i2.4423
Short DOI https://doi.org/g9hbsc
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