Advanced Machine Learning Techniques for Smart Grid Optimization and Energy Management

In recent years, the increasing global demand for energy, the proliferation of distributed energy resources, and the integration of renewable energy sources have underscored the need for smart, automated power distribution systems. Smart grids, as a new generation of energy infrastructure, enable real-time monitoring, control, and intelligent decision-making, replacing traditional centralized systems. In this context, Machine Learning (ML) and its advanced subfields—such as Deep Learning (DL), Reinforcement Learning (RL), Transfer Learning (TL), and Federated Learning (FL)—play a critical role in optimizing smart grid operations. These techniques leverage large-scale, complex datasets to enable accurate load forecasting, anomaly detection, demand response optimization, and energy management at both the consumer and producer levels. This paper provides a comprehensive review of how these advanced ML techniques are applied across various smart grid domains, highlighting their benefits, limitations, and potential research directions. Findings indicate that ML not only improves forecasting accuracy and reduces energy losses but also enhances the resilience, adaptability, and sustainability of the grid. Furthermore, integrating ML with emerging technologies such as the Internet of Things (IoT) and blockchain offers promising avenues for the future of intelligent energy management systems.

کلیدواژه ها:

Smart Grid ، Machine Learning ، Internet of Things ، Energy Management ، Blockchain ، Anomaly Detection in Power Systems