Neural Network Architectures in Smart Grid Management: Bridging Operational Efficiency and Grid Resilience in the Transition to Sustainable Energy

A key component of contemporary energy systems, smart grid management makes use of digital and intelligent technology to maximise the distribution of electricity, boost dependability, and increase efficiency. Neural network integration allows smart grid management to analyse large volumes of data, facilitating predictive maintenance, dynamic load balancing, and real-time analytics. Neural networks, particularly deep learning architectures, are perfect for demand forecasting, fault detection, and anomaly identification because they can simulate intricate, nonlinear interactions in power systems. The transition to a robust, decentralised, and customer-focused energy infrastructure is aided by this integration. For sustainable energy production and consumption, key applications include load forecasting, voltage stability analysis, and renewable energy management. Therefore, the application of neural networks improves smart grids' responsiveness and flexibility, resulting in lower operating costs and more effective energy consumption. This study sheds light on how neural networks have revolutionised smart grid infrastructure and shows how they may be used to solve changing energy-related issues. To further optimise smart grid performance, future paths will involve investigating hybrid models and sophisticated neural networks..