Day Ahead

Day-ahead forecasting focuses on predicting various energy-related variables, such as wind and solar power generation, electricity prices, and weather conditions, up to 24 hours in advance. Current research emphasizes improving forecast accuracy and uncertainty quantification using diverse machine learning models, including deep learning architectures like LSTMs, transformers, and normalizing flows, often incorporating techniques like transfer learning and conformal prediction. These advancements are crucial for optimizing energy market operations, enhancing grid stability through better renewable energy integration, and improving the efficiency and reliability of power systems.