Short Term Wind Speed

Accurate short-term wind speed forecasting is crucial for integrating wind energy into power grids and mitigating air pollution from industrial plants. Current research heavily emphasizes hybrid machine learning models, combining techniques like wavelet transforms, autoregressive models, support vector machines, recurrent neural networks (including LSTM and GRU variants), and deep learning architectures to improve prediction accuracy and efficiency. These advancements aim to reduce forecasting errors, leading to more reliable grid management and optimized industrial operations, ultimately enhancing renewable energy integration and environmental sustainability. The focus is on developing robust and adaptable models that handle the inherent non-linearity and variability of wind speed data, particularly in complex terrain.