An Optimized Approach for Predicting the Stability of the Smart Grid Utilizing Federated Learning: A Comparative Analysis

Assessing and predicting stability under various conditions is crucial, as stabilizing a Smart Grid (SG) is one of the key aspects for evaluating its performance. Intelligent techniques are needed to forecast stability and prevent unexpected instability in SG designs, especially with the growing number of residential and commercial buildings and the integration of renewable energy. Currently, predicting SG stability is challenging. In this context, Smart Grid with reliable mechanisms are being deployed to meet fluctuating energy demands and ensure greater availability. Consumer and producer involvement plays a significant role in influencing grid stability. This study proposed an optimized approach for identifying stability metrics in smart grids using deep learning frameworks. It introduced an optimized Federated Learning approach to predict Smart Grid sustainability. Alongside a practical evaluation and comparison with other methods, the paper presented implementation results for smart grid stability using hyper-parameter tuning. The results showed that the proposed an optimized Federated Learning model outperformed other deep learning techniques, achieving up to $99.73 \%$ accuracy, $99.73 \%$ precision, $99.73 \%$ recall, and a $99.73 \%$ F1 score value.