Optimizing Smart Grid Performance with Deep Learning Models

The smart grid is a cutting-edge power system idea that balances communication and electricity in system networks. It gives producers, operators, and consumers access to real-time information. The need to effectively manage electricity distribution to different consuming domains, including homes, businesses, industries, and smart cities, is growing. In this regard, dynamic power demand must be met by a stable smart grid system. Because there are so many affecting factors, predicting the stability of the smart grid is still difficult. Participation from producers and consumers is crucial since determining their level of involvement can help maintain grid stability. In this study, we suggest a deep learning model for smart grid stability prediction that is based on Gated Recurrent Units (GRU). Other conventional machine learning and deep learning classifiers. Such as Recurrent Neural Networks (RNN), Long ShortTerm Memory (LSTM), and Artificial Neural Networks (ANN), are contrasted with the outcomes of the suggested GRU model. With a 97.45% accuracy rate, our suggested GRU model outperforms previous models in predicting the stability of the smart grid.