Energy Modeling

Energy modeling aims to accurately predict and optimize energy consumption across various sectors, from buildings and urban environments to transportation and agriculture. Current research emphasizes developing data-driven models, leveraging machine learning techniques like deep learning (e.g., LSTMs, diffusion models), and agent-based simulations to improve prediction accuracy and handle complex, high-dimensional data, often addressing data scarcity through synthetic data generation or federated learning. These advancements are crucial for improving energy efficiency, promoting sustainable practices, and informing policy decisions related to energy consumption and renewable energy integration.