Optimal scheduling method of low-carbon energy supply chain based on machine learning and game theory

Abstract

Aiming at the quality and stability of a low-carbon new energy supply chain, this paper puts forward an optimal scheduling method for a low-carbon energy supply chain based on machine learning and game theory. By establishing an index and preprocessing keywords, the similarity between the index and the data in the platform database is calculated, and the data-sharing result of the low-carbon energy supply chain is obtained. This paper analyzes the game benefits of each subject in the optimization of the low-carbon energy supply chain, obtains the game matrix, and realizes the optimal scheduling of the low-carbon energy supply chain. The experimental results show that the scheduling convergence error of the low-carbon energy supply chain is low, the accuracy is high, and the load supply equipment can run stably.