Energy Management

Energy management research focuses on optimizing energy consumption and production across diverse systems, from individual buildings and vehicles to entire grids, aiming to improve efficiency, reduce costs, and minimize environmental impact. Current research heavily utilizes machine learning, particularly deep learning models like neural networks (including MLPs, GRUs, and CNN-BiLSTMs), reinforcement learning (including DDPG and MADDPG), and decision trees, often integrated with IoT frameworks and digital twins for real-time control and prediction. These advancements are crucial for addressing challenges related to renewable energy integration, grid stability, and sustainable transportation, with practical applications ranging from smart homes and buildings to electric vehicle management and industrial processes.