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
Reinforcement Learning and Tree Search Methods for the Unit Commitment Problem
Patrick de Mars
REAP: A Large-Scale Realistic Adversarial Patch Benchmark
Nabeel Hingun, Chawin Sitawarin, Jerry Li, David Wagner
Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning
Dongju Kang, Doeun Kang, Sumin Hwangbo, Haider Niaz, Won Bo Lee, J. Jay Liu, Jonggeol Na