The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

由于地面太阳辐照度固有的间歇性和可变性，太阳能发电系统的不断安装给地方和区域电网的运行带来了严峻挑战。近几十年来，针对小时内、日内和日前能源市场的太阳能预测方法已被广泛探索，作为具有成本效益的技术，以减轻太阳能不稳定对电网造成的负面影响。在这项工作中，对小时内太阳预报方法学的进展进行了全面回顾和简要总结。介绍了预测方法背后的理论以及这些理论如何应用于各种预测模型。回顾的数学工具包括回归方法、随机学习方法、深度学习方法、和遗传算法。审查的预测方法包括数据驱动方法、局部传感方法、混合预测方法以及生成概率预测和空间预测的面向应用的方法。此外，还提供了加速发展未来时段内预测方法的建议。