Predicting Smart Grid System Stability Using Machine Learning Techniques for Renewable Energy Sources

In order to ensure a sustainable future, renewable energy sources and smart grid systems are crucial. However, the dynamic nature of these systems makes it challenging to maintain their stability. This research work proposes the utilization of algorithms based on machine learning to model the grids considered as smart systems and predict their stability. The dataset used contains features related to sources of renewable energy and power consumption, with the constancy status of the smart grid system being the target variable. Five different algorithms of machine learning were appraised by means of working metrics like AUC, F1-score, precision and accuracy. The outcomes confirmed that the Decision Tree, Random Forest, and C4.5 algorithms achieved excellent execution with a correctness of 99.97% and 0.9997 AUC. This study features the effectiveness of algorithms of machine learning in modeling and forecasting the smart grid systems stability, which can assist in the design and development of further proficient and reliable renewable energy practices. The future scope of this study includes incorporating more advanced machine learning techniques and analyzing real-time data to enhance the accuracy of the model.