Wind Power

Wind power forecasting is crucial for integrating this intermittent renewable energy source into power grids reliably. Current research heavily utilizes machine learning, employing diverse architectures like deep neural networks (including U-Nets, LSTMs, CNNs, and graph neural networks), and hybrid models combining machine learning with physical models or statistical methods, to improve the accuracy and time horizons of wind speed and power predictions. These advancements aim to enhance grid stability, optimize energy management, and reduce reliance on fossil fuels by providing more precise and reliable forecasts across various timescales, from ultra-short-term to multi-decadal predictions. Furthermore, research addresses challenges like data scarcity, missing values, and data privacy concerns through techniques such as transfer learning, generative models, and federated learning.