Renewable Energy
Renewable energy research focuses on optimizing the integration of intermittent sources like solar and wind power into existing energy grids, aiming to improve forecasting accuracy, grid stability, and cost-effectiveness. Current research heavily utilizes machine learning, employing diverse models such as deep reinforcement learning, graph neural networks, and long short-term memory networks for tasks ranging from energy forecasting and grid management to optimizing energy storage and market participation. These advancements are crucial for enabling a sustainable energy transition, improving grid reliability, and reducing reliance on fossil fuels.
Papers
Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting
Ryan Lingo, Martin Arroyo, Rajeev Chhajer
Operational Wind Speed Forecasts for Chile's Electric Power Sector Using a Hybrid ML Model
Dhruv Suri, Praneet Dutta, Flora Xue, Ines Azevedo, Ravi Jain