Research on Intelligent Scheduling of Power Control Systems Based on Machine Learning

This paper delves into the application of machine learning in the intelligent scheduling of power control systems. In the context of the increasing complexity of power grids and the growing penetration of renewable energy, traditional scheduling methods face challenges. Machine - learning - based approaches, including deep neural networks, reinforcement learning, and ensemble learning, are proposed to address these issues. Mathematical models for power system scheduling are first established, and then corresponding machine - learning algorithms are designed and optimized. Extensive experiments are carried out using real-world power system data and simulation platforms. The results show that the proposed machine - learning - based intelligent scheduling methods can significantly improve the accuracy, efficiency, and adaptability of power system scheduling, reducing operating costs and enhancing grid stability.