Machine Learning Based Joint Operation of Multiple Devices Strategy Optimization Approach

This paper proposes a machine learning-based optimization method for the joint operation strategy of multiple equipment, aiming to improve the economic efficiency of the system that jointly supplies multiple types of equipment. Firstly, the background and development status of multiple pieces of equipment joint operation technology are analyzed, and the challenges and optimization space in equipment configuration, equipment modeling and operation strategy are pointed out. Secondly, a start-up threshold joint supply optimization model under the electricity-heat-determined operation strategy is designed, and a new optimization method for equipment operation efficiency is proposed. Then the integration of a reinforcement learning framework into the optimization system. Finally, through the analysis of arithmetic examples, the results demonstrate that the method can significantly improve the average operating efficiency of the equipment and minimize the operating costs of the system.