A Review on Deep Learning in Wind Speed Forecasting: Techniques and Challenges
Keywords:
Wind Speed Forecasting, Artificial Intelligence, Deep Learning, Wind EnergyAbstract
Wind speed forecasting is crucial for optimizing wind energy systems, enhancing turbine efficiency, ensuring grid stability, and planning energy production. This review critically examines advancements in wind speed forecasting through deep learning algorithms, which surpass traditional statistical methods in handling complex, non-linear, and non-stationary wind speed data. It focuses on various deep learning models, including CNNs, RNNs, LSTMs, and GRUs, and their ability to capture spatial and temporal dependencies. Essential data preprocessing techniques and evaluation metrics like RMSE, MAE, R, R2, and MAPE, are discussed to assess model performance. The review also synthesizes recent case studies demonstrating practical applications. Despite progress, challenges such as data quality, computational demands, overfitting, and model interpretability remain. Future research directions include improving data collection, developing efficient model architectures, enhancing interpretability, and mitigating overfitting. This review provides a concise overview of the current state of deep learning in wind speed forecasting, highlighting key methodologies, challenges, and future research opportunities.
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