A Review of Wind Power Prediction Methods Based on Deep Learning Algorithms

Abstract

With the acceleration of global energy transition, wind power, as an important renewable energy source, has been increasing its share in the energy structure. However, the intermittency and uncertainty of wind power bring challenges to the stable operation of power grids, so wind power prediction has become one of the key technologies for grid-connected wind power consumption. Firstly, the influencing factors, characteristics, and problem description of wind power prediction are analyzed; then the design of wind power prediction framework based on deep learning methods, classification of methods, and research directions are elaborated; then the evaluation indexes and methods of wind power prediction models are introduced; finally, the achievements and shortcomings of the current research are summarized, and the future development trend is outlooked. The review of wind power prediction methods based on deep learning algorithms can provide valuable references and lessons for related researchers and promote the further development of wind power prediction technology.