Abstract: Short-term photovoltaic power forecasting is the foundation for optimizing power dispatch in power systems, whose accuracy directly impacts the overall system efficiency. However, photovoltaic power, affected by multiple meteorological factors, exhibits short-term volatility and randomness, bringing severe challenges to high-precision forecasting. In recent years, with the rapid development of deep learning, it has shown excellent ability in excavating intrinsically correlated features, offering a promising technical path for precise forecasting. The application of deep learning in short-term photovoltaic power forecasting is systematically studied. First, the typical application paradigm of mainstream deep learning models is stated. Then, the application status and performance of deep neural networks, such as convolutional neural networks, recurrent neural networks, Transformer, and graph neural networks, in the task are discussed, under the ideal scenario of sufficient available data with static and no incremental changes. Furthermore, the strategies and progress of deep neural networks based on technologies such as deep data augmentation, transfer learning, federated learning, and online learning are analyzed in addressing real-world challenges such as data scarcity and limited access. Finally, the challenges regarding robustness, generalization, and adaptation faced by existing research are discussed, along with the future research routes from the perspectives of forecasting architecture, optimization strategies, and so on.

摘要： 短期光伏功率预测是电力系统实现优化调度的核心基础，其精度直接影响系统整体效能。然而，光伏发电功率受多重气象因素影响，所呈现出的短期波动性和随机性给高精度预测的实现带来严峻挑战。近年来，深度学习技术凭借其挖掘数据内在关联特征的卓越能力，为突破性提升短期光伏功率预测精度开辟了全新路径。全面梳理了现有深度学习技术在短期光伏功率预测任务中的应用进展。首先，阐述了深度学习在该预测任务中的典型应用范式；然后，介绍了在可用数据充足、静态且无增量的理想场景下，卷积神经网络、循环神经网络、转换器及图神经网络等深度神经网络在该任务中的应用现状；继而，剖析了基于深度数据增强、迁移学习、联邦学习、在线学习等技术的深度神经网络，在应对数据匮乏、访问受限等现实数据挑战时，在该任务中的应用思路与进展；最后，探讨了当前基于深度学习的短期光伏功率预测研究在鲁棒性、泛化性、适应性等方面面临的挑战，并从模型架构、优化策略等角度对未来技术路线进行了前瞻性展望。