Recent Hybrid Machine Learning Approaches in Wind Speed Forecasting—A Review
综述了近7年混合机器学习方法在风速预测中的应用，比较了不同模型的动机、方法、计算复杂度和性能提升，并讨论了经济影响和未来方向，对能源规划者和研究者有参考价值。
ABSTRACT Wind energy stands out as an increasingly popular energy source to mitigate the adverse effects of climate change. However, since wind energy is not continuous, the inability to predict how much energy can be produced at any time prevents further development of wind power generation. Therefore, wind speed forecasting studies are crucial to maximize the benefits of wind energy and facilitate accurate network planning, especially during peak usage periods. This paper comprehensively reviews hybrid machine learning studies forecasting wind speed in the last 7 years to gather insights and reveal better methods. Motivations, methodology, computational complexity, and performance improvement percentages of developed models over standard benchmark models are compared. Gathered insights, future directions, and the economic impacts of wind energy are also presented.