A Deep Learning Approach to Smart Grid Stability Prediction with LSTM Network

Smart grids play a crucial role in modernizing and optimizing energy distribution systems. Maintaining their stability is crucial to ensure uninterrupted energy supply and efficient grid operation. The problem addressed is the need for accurate and real-time prediction of stability disturbances, which is essential for proactive grid management. The paper represents a deep learning method utilizing Long Short-Term Memory (LSTM) networks for the prediction of smart grid stability. By employing extensive time-series data and a carefully designed LSTM architecture, our model effectively anticipates grid stability disruptions, contributing to improved grid reliability and energy distribution. The findings of this research hold the potential to significantly enhance the stability and efficiency of smart grids, ultimately benefiting both utility companies and consumers. Our primary objective is to develop an LSTM-based model capable of accurately predicting grid stability, and we have achieved a core result with an impressive accuracy rate of 96%.