The use of wind power, a pollution-free and renewable form of energy, to generate electricity has attracted increasing attention. However, intermittent electricity generation resulting from the random nature of wind speed poses challenges to the safety and stability of electric power grids when wind power is integrated into grids on large scales. Therefore, accurate forecasting of wind speed and wind power (WS/WP) has gradually taken on a key role in reducing wind power fluctuations in system dispatch planning. With the development of artificial intelligence technologies, especially deep learning, increasing numbers of deep learning-based models are being considered for WS/WP forecasting due to their superior ability to deal with complex nonlinear problems. This paper comprehensively reviews the various deep learning technologies being used in WS/WP forecasting, including the stages of data processing, feature extraction, and relationship learning. The forecasting performance of some popular models is tested and compared using two real-world wind datasets. In this review, three challenges to accurate WS/WP forecasting under complex conditions are identified, namely, data uncertainties, incomplete features, and intricate nonlinear relationships. Moreover, future research directions are summarized as a guide to improve the accuracy of WS/WP forecasts.

风力发电是一种无污染、可再生的能源，用于发电越来越受到关注。然而，当风电大规模并网时，风速随机性导致的间歇性发电对电网的安全和稳定性提出了挑战。因此，准确预测风速风功率（WS/WP）逐渐在系统调度规划中起到降低风电波动的关键作用。随着人工智能技术，尤其是深度学习的发展，越来越多的基于深度学习的模型因其处理复杂非线性问题的卓越能力而被考虑用于 WS/WP 预测。本文全面回顾了 WS/WP 预测中使用的各种深度学习技术，包括数据处理阶段、特征提取阶段和关系学习阶段。使用两个真实世界的风数据集测试和比较了一些流行模型的预测性能。在这篇综述中，确定了复杂条件下准确 WS/WP 预测的三个挑战，即数据不确定性、不完整的特征和复杂的非线性关系。此外，总结了未来的研究方向，以指导提高 WS/WP 预测的准确性。确定了复杂条件下准确 WS/WP 预测的三个挑战，即数据不确定性、不完整的特征和复杂的非线性关系。此外，总结了未来的研究方向，以指导提高 WS/WP 预测的准确性。确定了复杂条件下准确 WS/WP 预测的三个挑战，即数据不确定性、不完整的特征和复杂的非线性关系。此外，总结了未来的研究方向，以指导提高 WS/WP 预测的准确性。