Dynamic and Transformative Hybrid Machine Learning Strategies for Effective Stability Prediction in Smart Energy Networks

Smart grid stability prediction (SGSP) is essential for ensuring reliable energy delivery and protecting critical infrastructure. Fluctuations in generation, load, and demand create significant challenges in maintaining stable and affordable electricity. The research presents a hybrid approach to SGSP by integrating Logistic Regression, Decision Tree, and Random Forest models using a hard voting method. This hybrid model achieves 99% accuracy, significantly outperforming standalone methods. Among the individual algorithms, Random Forest demonstrates strong performance with 98.7% accuracy, showcasing its reliability for complex data handling. The proposed approach enhances stability predictions, providing robust support for smarter and safer grid operations. By ensuring stable energy delivery, the system facilitates the seamless integration of renewable energy sources, contributing to sustainable energy management. Extensive validation confirms the effectiveness of the proposed model and its potential to address critical challenges in modern energy systems. These contributions aid utility providers, policymakers, and consumers by creating a more reliable and efficient energy infrastructure, paving the way for a resilient and future-ready grid.