Wind Speed Forecasting

Accurate wind speed forecasting is crucial for integrating intermittent renewable energy sources, like wind power, into electricity grids and optimizing energy production. Current research heavily emphasizes hybrid machine learning models, often combining techniques like Temporal Convolutional Networks (TCNs), Transformers, and various support vector machines, with signal processing methods for data preprocessing and noise reduction to improve forecast accuracy across short-term to medium-term horizons. These advancements aim to enhance grid stability, reduce reliance on fossil fuels, and improve the efficiency of wind energy utilization. The development of more precise forecasting models directly impacts the economic viability and environmental sustainability of renewable energy deployment.