Graph Neural Networks (GNNs) have emerged as a powerful tool for wind speed prediction due to their ability to model the complex spatial and temporal relationships within wind turbine data. GNNs leverage graph structures to represent wind turbines and their connections, capturing dependencies and interactions within the wind farm. By incorporating various input features, such as wind speed, direction, shear, and weather conditions, GNNs learn patterns and extract insights from historical data to predict future wind speeds with high accuracy.
