Advancing Smart Grid Security and Stability Through Machine Learning Techniques
Nayeemuddin MOHAMMED, Hiren MEWADA, Tasneem SULTANA, Syam Sundar LINGALA
Abstract. Smart grids with increased operational efficiency and bidirectional energy flows.. Resilience has been developed as a result of a complete shift in the responsibilities of energy market participants. The transition from a traditional energy ecosystem to a more dynamic system, in which end users can both consume and produce energy, has led to more dynamic interactions. The redundant stability ensures that power stays in the range, maintaining safe infrastructure. To determine the stability, three different machine learning algorithms were employed and compared. The results reveal that the Random Forest model achieved an R² of 0.99647 on training and 0.97143 on testing. The Gradient Boosting model achieves a score of 0.9848 for training and 0.98 for testing. In addition, the Bayesian Ridge model performed well, with 0.9963 on training and 0.971 on testing. These results also indicate that Random Forest performed consistently across all. GB and Bayesian Ridge perform well with slightly higher error rates than RF. In addition, new and advance methods of energy management and distribution are required, utilizing more sophisticated technologies, such as advance machine learning, deep learning to enhance grid stability and predictability to incorporate renewable energy.
Keywords
Grids, Stability, Prediction, Reliability, Security, Algorithm, Training, Testing
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Nayeemuddin MOHAMMED, Hiren MEWADA, Tasneem SULTANA, Syam Sundar LINGALA, Advancing Smart Grid Security and Stability Through Machine Learning Techniques, Materials Research Proceedings, Vol. 64, pp 268-275, 2026
DOI: https://doi.org/10.21741/9781644904091-33
The article was published as article 33 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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