Abstract:The power industry is in a critical period of intelligent transformation, and the research of grid dispatch prediction models and strategies has become an important force to promote this process. This paper reviews the application of artificial intelligence technologies in grid scheduling, focusing on the key role of machine learning, deep learning and reinforcement learning algorithms in forecast model construction and strategy optimisation. The article delves into the significant advantages of these techniques in handling large-scale data, real-time data analysis and prediction, and elaborates on the key techniques and computational methods required to construct efficient prediction models. Further, the paper presents the application of AI in multi-objective optimisation and real-time scheduling strategies, highlighting its potential for improving grid operational efficiency and enhancing grid safety and security. Finally, an outlook on the future direction of grid scheduling intelligence is provided, including the enhancement of fundamental theoretical research, the construction of a collaborative research environment, the deepening of technological applications, and the focus on technological ethics and security topics. The innovation of this paper lies in the deep integration of AI technology and grid scheduling, which provides new perspectives and methods for the intelligent development of the power industry.