AI-Driven Approaches to Power Grid Management: Achieving Efficiency and Reliability

The main objective of this research is to improve the efficiency, reliability, and security of the power grid through the integration of artificial intelligence (AI) techniques. The research method involves developing an integrated AI-SGMS framework, including: (1) AI-based Load Forecasting using LSTM and transformer models; (2) Reinforcement Learning for Network Optimization with deep reinforcement learning (DRL) agents; (3) AI-enabled Fault Detection using CNN and autoencoder; (4) AI-driven Intrusion Detection System (IDS) for cybersecurity; and (5) Edge Computing for Decentralized Decision Making. The results show that AI-SGMS is able to optimize energy distribution, improve predictive maintenance, strengthen cybersecurity, and enhance network resilience. The system reduces waste, prevents congestion, detects potential failures, and mitigates cyber threats. Decentralized decision-making ensures rapid response and network resilience. The conclusion of this research is that the application of AI in power grid management, such as AI-SGMS, has the potential to revolutionize energy distribution, reduce operational costs, and support the transition to a sustainable, resilient, and efficient power grid. This research provides a foundation for broader development of AI solutions in power grid management.