Abstract: Building a new power system is one of the main measures to achieve the "carbon peak and carbon neutrality" strategic goal. Wind power and photovoltaic power generation are the most representative renewable energy, and their volatility and randomness have brought major challenges to grid security and renewable energy consumption; while renewable energy power prediction is the key technology to reduce its random impact. In recent years, with the successful application of big data technology and the latest artificial intelligence (AI) technology represented by deep learning and reinforcement learning in many fields, its application in renewable energy power forecasting is still in the ascendant. This paper first briefly discusses the theoretical basis of AI technology in the application of renewable energy power forecasting, and systematically summarizes the application of AI technology in wind power and photovoltaic power forecasting, including the application of various data processing technologies such as data enhancement and feature construction, application of traditional machine learning, deep learning, combined algorithms in model construction, the application of evolutionary algorithm, swarm intelligence optimization, reinforcement learning, and other intelligent optimization algorithms in model training and hyperparameter optimization. Then, the current related literature is statistically analyzed. Based on the results of the renewable energy forecasting contest as well as the actual forecasting system research situation, the current academic research hotspots and trends, and the application of industrial models are compared and analyzed. Finally, some problems existing in the current renewable energy power forecasting in scene adaptation, few-shot learning, numerical weather prediction (NWP) data spatio-temporal resolution, distributed renewable energy prediction, etc. are analyzed, and the prospects of a variety of AI technologies such as reinforcement learning, meta-learning, graph neural network(GNN), to solve related problems are prospected.