Generation Data

Generation data research focuses on accurately predicting and managing the intermittent output of renewable energy sources like solar and wind power. Current efforts concentrate on improving forecasting accuracy using advanced machine learning models, such as deep neural networks (including transformers and recurrent architectures like LSTMs), and incorporating physical principles (physics-guided machine learning) and spatial-temporal relationships (graph neural networks) into these models. This work is crucial for optimizing grid stability, enhancing energy management strategies, and facilitating the wider adoption of renewable energy technologies by improving the reliability of renewable energy forecasts.