Overview of Applications and Research Directions of Deep Learning Methods for Wind Power Prediction

As the global demand for renewable energy increases, wind power, as an important part of clean renewable energy, the accurate prediction of its power is crucial for the stable operation of the power system and the efficient use of energy. In recent years, deep learning methods have demonstrated significant advantages in the field of wind power prediction, and by constructing complex nonlinear models, deep learning models can effectively capture the intrinsic laws and changing trends of wind power data. This paper outlines the research objectives of wind power prediction from the classification of wind power prediction, the general idea of implementation, and the evaluation method. The application of deep learning technology in wind power prediction is reviewed, and on the basis of making a careful division of deep learning technology, it focuses on analyzing the overcome problems and performance by spatial structure-based deep learning models and time-based deep learning models and their related variants, and summarizes the limitations of the proposed modeling methods and the corresponding solutions. In addition, research progress in deep learning-based wind power prediction is outlined in data processing, parameter optimization algorithms, and optimization methods for wind power prediction models. Finally, an outlook on the future development direction of wind power prediction is given.