Wind Speed

Accurate wind speed forecasting is crucial for optimizing renewable energy integration into power grids and mitigating risks in various sectors, from aviation to agriculture. Current research heavily emphasizes the development and application of advanced machine learning models, including hybrid approaches combining neural networks (like LSTMs, Transformers, and Graph Neural Networks) with other techniques such as wavelet transforms and singular spectrum analysis, to improve prediction accuracy across various temporal and spatial scales. These efforts focus on enhancing model robustness to noise, improving the representation of spatiotemporal dependencies, and accurately predicting extreme wind events. The resulting improvements in forecasting precision have significant implications for grid stability, resource management, and risk assessment.